ORIGINAL RESEARCH article
Sec. Performance Science
Volume 8 - 2017 | https://doi.org/10.3389/fpsyg.2017.01709
Rhythmic Density Affects Listeners' Emotional Response to Microtiming
- 1School of Music, Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland
- 2Department of Social Sciences and Cultural Studies, Institute of Musicology and Music Education, Justus-Liebig-University Giessen, Giessen, Germany
- 3Media Psychology Department, SRH Hochschule der Populären Künste, Berlin, Germany
Microtiming has been assumed to be vital for the experience of groove, but past research presented conflicting results: some studies found that microtiming is irrelevant for groove, others reported that microtiming has a detrimental effect on the groove experience, yet others described circumstances under which microtiming has no negative impact on groove. The three studies in this paper aim at explaining some of these discrepancies by clarifying to what extent listeners' emotional responses to microtiming depend on the distribution of microtiming deviations across instrumental parts (voicing) or other moderating factors like tempo or rhythmic density. The studies use data from two listening experiments involving expert bass and drums duo recordings in swing and funk style.
– Study A investigates the effect of fixed time displacements within and between the parts played by different musicians. Listeners (n = 160) reacted negatively to irregularities within the drum track, but the mutual displacement of bass vs. drums did not have an effect.
– Study B develops three metrics to calculate the average microtiming magnitude in a musical excerpt. The experiment showed that listeners' (n = 160) emotional responses to expert performance microtiming aligned with each other across styles, when microtiming magnitude was adjusted for rhythmic density. This indicates that rhythmic density is a unifying moderator for listeners' emotional response to microtiming in swing and funk.
– Study C used the data from both experiments in order to compare the effect of fixed microtiming displacements (from Study A) with scaled versions of the originally performed microtiming patterns (from Study B). It showed that fixed snare drum displacements irritated expert listeners more than the more flexible deviations occurring in the original performances. This provides some evidence that listeners' emotional response to microtiming deviations not only depends on the magnitude of the deviations, but also on the kind and origin of the microtiming patterns (fixed lab displacements vs. flexible performance microtiming).
Microtiming in music denotes a wide range of time-related phenomena that arise as a consequence of conflicting concepts of musical time. Music theory describes the organization of time in metered Western popular or art music using a few discrete, rationally related time duration categories (Hasty, 1997; London, 2004). The canonical notation of rhythm in Western music epitomizes this principle: the relation between the duration of different rhythmic values can be expressed as small-integer ratios. Given a specific tempo, a sequence of notes of the same rhythmic value (e.g., quarter notes) is thought to be isochronous. And notes written on the same metric position (e.g., chords) are supposed to begin synchronously. Performed music, however, plays in continuous physical time where musical events may happen at any moment. In performance, the occurrence of musical events shows considerable temporal freedom, and time intervals between events are variable. This fundamental discrepancy between theoretical, fixed, discrete duration categories and the flexible nature of music performance in continuous time broadly defines the domain of microtiming in metered Western music.
Microtiming phenomena are very diverse and differ across musical contexts. Some manifestations of microtiming are relatively large-scale, can easily be detected by the untrained ear, and have become characteristic for certain musical styles. The asymmetric subdivision of the beat, for example, is a frequent feature of both jazz (for an overview on the swing ratio literature, see Friberg and Sundström, 2002; Dittmar et al., 2015; Camara, 2016) and baroque music (Moelants, 2011). Further, expressive timing (i.e., local tempo variations) is a phenomenon widely observed in the performance of Western art music. It has been associated with phrase structure, harmonic and melodic development, and it is understood to be a major source of expressiveness in Western art music (Clarke, 1982; Cook, 1987; Repp, 1992, 1997, 1998b; Beran and Mazzola, 2000; Hong, 2003; Sundberg, 2003; Cheng and Chew, 2008; Senn et al., 2009, 2012; Dodson, 2011; MacRitchie, 2011; Benadon and Zanette, 2015).
A multitude of musical or contextual factors may be associated with microtiming phenomena. Tempo, for example, has been shown to be related with microtiming magnitude: Friberg and Sundström (2002) found that the swing ratio (i.e., the duration ratio between the long first and the short second swing eighth note) is negatively correlated with tempo. Moelants (2011) observed a comparable relationship for the baroque notes inégales, and Repp (1995) reported a similar tendency for the execution of expressive timing in Nineteenth century piano music: faster tempo implied less microtiming (see also Fraisse, 1956; Repp et al., 2002).
Bengtsson (1974) showed that the first quarter note of a Viennese Waltz measure tends to be longer than the second and third quarter notes. This can be associated with the dance movements: the basic Waltz step pattern begins with a long step, followed by two shorter ones. The microtemporally adapted beat durations help dancers to map their steps to the music more easily. Hence, the fact that the Waltz is used for dancing has shaped microtiming properties of the music.
Finally, the use of tempo rubato in the performance of Western art music piano repertoire has changed through performance history. Pianists of the early Twentieth century used ample rubato, but it fell out of favor during the second half of the century (Philip, 2004). Influential performers played a role (e.g., Glenn Gould and his recordings of works by J. S. Bach) but also new performance paradigms (such as the historically informed performance movement). In this case, the taste and aesthetics of the performers and of their audiences were contextual factors that modified the use of a microtiming-based performance technique like tempo rubato.
Within the same context, microtiming patterns might be so stable that they can even be cast into fixed performance rule systems (Beran and Mazzola, 2000; Friberg et al., 2006). Listeners' familiarity with such stable patterns may also affect their sensitivity to microtiming. Repp exemplified this in a striking way: he showed that listeners of Chopin's Etude op. 10, No. 3, had “obligatory expectations” with respect to the microtiming pattern, depending on phrase structure and an implicit rule system (Repp, 1998a).
The pioneers of technology-based timing analysis, Ingmar Bengtsson and Alf Gabrielsson, discussed the question to what extent small-scale microtiming phenomena are a meaningful part of the performance or just motor noise resulting from humans' limited capacity to realize a “quantized” music performance, i.e., a performance with perfect mechanical regularity (Gabrielsson, 1974; Bengtsson and Gabrielsson, 1977, 1980, see also Rasch, 1988). A large body of research on sensori-motor synchronization (for an overview of the tapping literature, see Repp, 2005; Repp and Su, 2013) studied the production side of this question, namely humans' timing precision when synchronizing body motion with acoustic stimuli. Some of these studies addressed the precision of expert drummers' performance (Fischinger, 2009; Fujii et al., 2011; Kilchenmann and Senn, 2011) and discussed methods to distinguish between unsystematic, random timing variations that are merely motor noise, and systematic, recurring variations that may be characteristic for a style and relevant for the effect of the music (Hellmer and Madison, 2015).
The perception of microtiming has also been studied: a substantial body of work discusses the precision of human timing discrimination with respect to auditory stimuli (Michon, 1964; Fraisse, 1967; Nordmark, 1968; Abel, 1972; Lunney, 1974; Halpern and Darwin, 1982; Hibi, 1983; Nakajima, 1987; Hirsh et al., 1990; Nakajima et al., 1992; Drake and Botte, 1993; Hoopen et al., 1995; Sasaki et al., 1998; Ehrlé and Samson, 2005; Thomas, 2007). In their influential study, Friberg and Sundberg (1995) found that the Just Noticeable Difference (JND) for microtiming deviations in a monotonic, isochronous sequence was approximately constant at an absolute value of 6 ms for inter-onset-intervals (IOIs) smaller than 240 ms. The JND remained stable at roughly 2.5% of the IOI when the interval was in the range 240–1,000 ms. Hence, above the 6 ms threshold, listeners' sensitivity to microtiming deviations appears to follow Weber-Fechner's law. JND research investigated listeners' time discrimination in a laboratory setting that is optimized for the detection of minimal deviations. In everyday listening situations, however, performed music is usually more complex than the lab stimuli, and listeners' threshold for detecting microtiming deviations in a regular listening context is likely to differ from the JND's.
In recent years, the impact of microtiming on the groove phenomenon has been a major research topic. Relevant papers in music psychology define groove as an enjoyable inner urge to synchronize body movement with the beat of the music (Janata et al., 2012; Witek et al., 2014). This shifts the focus from the perceptual detectability or noticeability of microtiming to bodily entrainment and emotional responses.
The hypothesis that microtiming is essential for triggering groove originated in Charles Keil's Theory of Participatory Discrepancies or PD Theory (Keil, 1987, 1995, 2010). It has considerable support among musicians (Berliner, 1994; Monson, 1996; Doffman, 2008). The empirical evidence concerning the relevance of microtiming for groove, however, has been inconclusive so far: some studies found no evidence that microtiming influences groove (Butterfield, 2010; Madison et al., 2011; Madison and Sioros, 2014), others found that microtiming is detrimental to groove (Davies et al., 2013; Frühauf et al., 2013; Matsushita and Nomura, 2016); yet others found that microtiming patterns arising in competent performance do not affect groove negatively, but if the patterns are exaggerated in magnitude, the groove experience decreases (Kilchenmann and Senn, 2015; Senn et al., 2016). Finally, Hofmann et al. (2017) suggested that listeners preferred tightened microtiming patterns to the microtiming magnitude of the originally performed music.
How can we explain the discrepancies between these empirical results? Previous research has primarily focused on the magnitude of microtiming deviations. The patterning of the microtiming deviations have received little attention up to now. This paper presents three small studies that aim at exemplifying how patterning aspects may impact listeners' emotional responses to microtiming.
– Study A investigates to what extent the distribution of microtiming deviations across instrumental parts affects listeners' emotional responses. Previous studies have presented two methods for introducing fixed timing displacements into otherwise quantized musical stimuli: one method is to shift the entire parts played by different instruments against each other, for example a bass track against the corresponding drum track (as used in Butterfield, 2010; Matsushita and Nomura, 2016). The other method consists in displacing events against each other that are played by the same instrument: Frühauf et al. (2013), for example, displaced the snare drum or bass drum events relative to the rest of the drum track. The first method leaves the relationships within an instrumental part intact, whereas the second method introduces a temporal disturbance into a part. In Study A, both manipulation methods are applied to duo bass and drums recordings that have first been presented in Kilchenmann and Senn (2015) and used again in Senn et al. (2016). This allows for the comparison of the two manipulation methods' effects on the basis of the same musical examples, and it allows to verify the results of Butterfield (2010) and Frühauf et al. (2013).
– Study B uses data from an earlier experiment (Senn et al., 2016) to investigate how the magnitude of microtiming deviations can be quantified in a way that is meaningful and potentially uniform across different musical contexts. In Senn et al. (2016), the originally performed microtiming patterns of swing and funk duo performances (bass/drums) were down- or up-scaled by fixed percentages. The original swing performance showed greater average microtiming magnitudes in milliseconds than the funk performance. These differences in magnitude were accentuated when they were upscaled. But surprisingly, listeners' responses were indistinguishable across the two styles regardless of the scaling. This suggested that listeners expected to hear larger microtiming deviations in swing than in funk. The re-analysis of the data aims at studying how well listener responses align across styles when they are modeled as a function of three different measures for microtiming magnitude: the Standard Timing Deviation measures the mean deviations in milliseconds. The Tempo-adjusted Standard Timing Deviation accounts for tempo differences, treats tempo as a potential moderator variable, and measures microtiming magnitude as a proportion of the beat. The Density-adjusted Standard Timing Deviation considers rhythmic density as a moderator and measures microtiming as a proportion of the mean IOI of the music.
– Study C, finally, uses the data from Studies A and B to compare the effects of different microtiming patterns on listeners' emotional response. It asks specifically, whether listeners react more sensitively to the lab-generated, fixed microtiming displacements of Study A in comparison to the microtiming patterns that arose in the original performance, as used in Study B.
2. Study A: Microtiming between and within Sound Sources
Study A compares the groove-related effects of two laboratory-generated methods for systematically introducing fixed microtiming deviations into quantized music. The two manipulations affect entire instrumental parts or voices (i.e., layers of the musical fabric that are characterized by specific timbres) differently: the Shift manipulation displaces parts that are played by different musicians against each other by a certain time offset (between musicians). The Disturbance manipulation displaces voices against each other that are played by the same musician (within musician).
Butterfield (2010) used the Shift method: he worked with standard-practice jazz rhythm section examples (bass and drums). He displaced the drum tracks and the bass track relative to each other by either anticipating the bass onsets by 10, 20, or 30 ms relative to the drums (bass lead) or by conversely anticipating the drum onsets by 10, 20, or 30 ms (drums lead) relative to the bass. Butterfield found that this kind of manipulation had little effect on listeners.
Frühauf et al. (2013) took the Disturbance approach: they used a generic rock/pop drum pattern (eighth notes on the hi-hat, downbeats on the bass drum, and backbeats on the snare drum) and manipulated one layer within the drum track. They displaced either all bass drum or all snare drum sounds uniformly by −25, −15, 0, 15, or 25 ms, while keeping the remaining instruments in place. Frühauf et al. found a dose-response effect of the displacements on listeners' groove ratings: the perfectly quantized stimulus (0 ms displacement) obtained the highest groove ratings. Ratings decreased with larger displacements in either direction (early or late) and for either of the instruments. However, this effect was stronger when the snare drum was displaced instead of the bass drum, and when the displaced event onsets were early instead of late.
Matsushita and Nomura (2016) proceeded according to the Shift method. They used the same standard rock/pop drum pattern as Frühauf et al. (2013), but combined it with repeated eighth notes on a bass guitar. Compared to Butterfield (2010), they increased the magnitude of the time shift considerably: the bass voice was displaced by −62.50, −46.88, −31.25, 0, +31.25, +46.88, and +62.50 ms (negative numbers refer to bass lead, positive numbers to bass lag). They found that groove ratings were high for well synchronized stimuli, and declined with larger displacements in both bass lead and bass lag direction; these results were similar to those reported by Frühauf et al. (2013).
The goal of Study A is to replicate the main results of Butterfield (2010) and Frühauf et al. (2013) by applying both displacement methods to the swing and funk examples from Kilchenmann and Senn (2015). In particular, we try to verify, whether introducing a Disturbance microtiming pattern by displacing the snare drum layer against the other layers (the remaining drum tracks and the bass) affects the groove experience negatively (comparable to the effects found by Frühauf et al., 2013), and whether introducing a time Shift microtiming pattern between bass and drums has little or no effects on listeners (comparable to Butterfield, 2010).
2.1. Materials and Methods
The stimuli for both experiments reported in the present paper were derived from two recorded studio performances played by bassist Wolfgang Zwiauer and drummer Dominik Burkhalter, two internationally renowned performers on their respective instruments, professors at the Lucerne University of Applied Sciences and Arts, and musical collaborators for many years. The studio session was organized by the researchers, and its only purpose was to create two recordings from which experimental stimuli would subsequently be derived. In one of the two recordings, the musicians extemporized a funk pattern over an eight-bar vamp of their own invention, at a tempo of 100 bpm. In the other recording, the musicians improvised a swing pattern over a twelve-bar harmonic model at a tempo of 150 bpm. The chosen tempi are typical medium tempi within their respective genre contexts. Each original recording had a duration of approximately three minutes. The drummer played an acoustic drum set. The bassist played an electric bass for the funk recording and an acoustic bass guitar for the swing recording.
The musicians wore headphones during the studio performances. In the monitor mix, they heard the performed music and a metronome click as a common beat reference. The musicians sat acoustically separated in different recording booths and had direct visual contact through a glass panel. From previous studio work, the musicians were used to hearing a metronome click while performing, and they declared to be comfortable with this setup. After the recording session, the musicians indicated passages in each recording that they considered to have the best groove. From these passages, the researchers subsequently chose one iteration of the swing and funk patterns as a basis for the timing manipulations; the musicians agreed with this choice. These selections of 20 s duration have been used in previous studies (Kilchenmann and Senn, 2015; Senn et al., 2016) for the creation of experimental stimuli.
Transcriptions of the selected passages can be studied in Figure 1, 2. These transcriptions were made after the recording session, and they are of purely descriptive nature (Seeger, 1958). The musicians verified the accuracy and ecological validity of the transcriptions. The originally performed onset times were measured using the LARA analysis software1. An equidistant metronomic grid was derived from the click track, which defined a quantized onset time for each event of the performance. For swing, the mean swing ratio of the recording was used to determine the offbeat eighth notes grid positions. The mean swing ratio was 2.66, which is typical for jazz at 150 bpm (Friberg and Sundström, 2002; Dittmar et al., 2015). The transcriptions show the timing differences between the performed events and the corresponding position on the metronomic grid in milliseconds (negative numbers indicate that the performed onset was early; positive numbers indicate late onsets).
Figure 1. Transcription of the recorded 12-bar Swing pattern used as a basis for the timing manipulations. Originally performed timing deviations from metronomic time are indicated in milliseconds (negative numbers, event is ahead of the metronome; positive number, event sounds later than the metronome); S.D., snare drum; H.H. ped, foot-operated hi-hat cymbal; R.C., ride cymbal.
Figure 2. Transcription of the recorded 8-bar Funk pattern used as a basis for the timing manipulations. Originally performed timing deviations from metronomic time are indicated in milliseconds (negative numbers, event is ahead of the metronome; positive number, event sounds later than the metronome); S.D., snare drum; H.H., hi-hat cymbal; B.D., bass drum.
The drum audio track was replaced by an accurate reconstruction using the Massey Drum Replacement Tool (version 3.9) and samples from the Toontrack Superior Custom and Vintage library (version 2.3.1). This replacement was necessary in order to avoid creating acoustic artifacts when manipulating the timing of the events: such artifacts are a consequence of poor source separation when recording the different instruments of the drum set with several microphones. After replacement, the researchers obtained highly accurate replicas of the two selected passages that could be subjected to timing manipulations. No replacement was necessary for manipulating the timing of the bass line: Avid Pro Tools' (version 10.0.0) time stretching function was applied to the originally recorded bass audio track in order to create the bass tracks of the experimental stimuli. For Study A, all events were first adjusted to coincide exactly with the metronomic grid. Hence the subsequent Shift and Disturbance timing manipulations departed from perfectly quantized stimuli. (The original performance microtiming patterns will be used in Study B.)
In order to emulate the time Shift between the two instruments, as used by Butterfield (2010), a series of 14 experimental stimuli was created, in which the entire bass voice was displaced relative to the drums track by −24, −16, −8, 0, +8, +16, or +24 ms (seven stimuli for each style). The time shift manipulation introduced a time offset between the two instrumental parts, but the perfect quantization of the events within the bass and drums tracks was left intact.
In order to introduce Disturbance into the music (similar to Frühauf et al., 2013), 14 stimuli with timing irregularities were created by displacing the snare drum events by −24, −16, −8, 0, +8, +16, or +24 ms, while all the other events remained at their exact quantized positions (i.e., the bass and all other instruments of the drum set).
In summary, 28 experimental stimuli were derived from recordings in two Styles (Funk and Swing) by applying two microtiming manipulation methods (Shift and Disturbance) and seven different Displacement Magnitudes (−24, −16, −8, 0, +8, +16, or +24 ms) for each method. The stimuli for Study A can be downloaded from the Supplemental Material section of this article.
A total of 160 participants took part in the listening experiment, 75 female and 85 male. One hundred and forty participants were students of Lucerne University of Applied Sciences and Arts, 18 were enrolled at Lucerne University, the remaining 2 participants were not affiliated with any University. Eighty two participants were considered to be music Experts because they were either enrolled in a program to become professional musicians or music teachers, or they had previously completed such a program. The remaining 78 participants did not meet either of these criteria and were thus considered to be musical Non-Experts. The participants were predominantly young adults ranging from 18 to 47 years (, s = 4.5). They were recruited via emails, class visits, and personal contacts. Since the questionnaires were in German, only fluent German speakers were recruited.
The experiment was carried out in a quiet office room at Lucerne School of Music. Participants were seated at a desk; stimuli were played from a personal computer (running the Neurobs Presentation Software, version 16, on Windows 7) through a Presonus Firebox audio interface and AKG Mk II headphones. The participants took the test one at a time. Written instructions informed each participant about the procedure of the experiment and about the possibility of aborting the experiment at any time. Participants adapted the size of the headphones and adjusted the playback volume to a comfortable level.
The experiment started with practice trials. Each participant assessed four test examples, and could ask questions to the experimenter if any aspect of the rating procedure or the navigation was unclear. When the participant declared to be familiar with the procedure, the investigator left the room, while the participant listened to the experimental stimuli and rated them using an on-screen rating form. In order to avoid style preference effects due to direct comparison, each participant listened to the stimuli of only one randomly assigned Style. The sequence of the 14 stimuli (seven in the Shift series, and seven in the Disturbance series) was randomized to counteract the effects of fatigue and familiarity. Design and procedure of the experiment were approved by the Ethics Committee of the Canton of Lucerne.
2.1.4. Psychometric Measures
Participants rated the stimuli using the Emotional Assessment of Groove (EAG) questionnaire. The EAG was constructed by the authors and was designed to capture emotional responses connected to the groove experience. It was validated in a pre-study at Justus-Liebig-University Giessen and used in a previous study (Senn et al., 2016). The questionnaire collects listeners' feedback on three scales that measure the strength of emotional reactions connected to the experience of groove: Entrainment (four items, Cronbach's α = 0.89), which measures how much the music stimulates the urge for body movement in listeners; Enjoyment (five items, Cronbach's α = 0.88), which measures the pleasure a participant experiences while listening to a stimulus. A third scale captures the participants' experience of unnaturalness or Irritation (four items, Cronbach's α = 0.94) while listening. This scale inversely measures the experience of effortlessness or fluency that has been associated with groove (Janata et al., 2012). The mental experiences captured by the three scales comply with the four-dimensional definition of emotions by Cabanac (2002).
Additionally, the well-established pictorial Self Assessment Manikin (SAM) was used to measure the affective reactions of participants to each listening experience (Bradley and Lang, 1994; Backs et al., 2005). The SAM measures affective reactions on three scales: Valence (happy vs. unhappy), Arousal (quiet vs. excited), and Dominance (powerful vs. powerless). It has been previously used for measuring reactions to music (Gomez and Danuser, 2007; Senn et al., 2016).
2.1.5. Statistical Design
Quadratic regression models were fitted to the data in order to test for effects of the seven timing Displacement Magnitudes (−24, −16, −8, 0, +8, +16, +24 ms) on any of the EAG (Entrainment, Enjoyment, Irritation) or SAM (Valence, Arousal, Dominance) outcome variables. The analysis was carried out separately for each of the two timing manipulation methods (Shift, Disturbance). The inclusion of quadratic regression coefficients was based on previous results that parabola-shaped regression models appear to best approximate this kind of data (see Frühauf et al., 2013; Matsushita and Nomura, 2016), compared to first-order linear regression models. The between-subjects variables Style (Funk, Swing) and Expertise (Experts, Non-Experts) did not show any effect on the outcome variables, hence they will be omitted in the Results section of Study A.
The presentation of the 14 stimuli as one randomized sequence caused a subtle, but serious methodological problem: the two quantized stimuli with 0 ms deviation in both the Shift and the Disturbance series were identical. Consequently, the association of the ratings with a stimulus of either series is purely coincidental. In order to solve this problem, the responses to the quantized stimuli were discarded from the dataset prior to the analysis.
The overall significance probability level was set to α = 0.05. With two displacement timing manipulation methods and six dependent variables (three each for EAG and SAM), a total of twelve independent models was fitted to the data. Šidàk correction was applied for familywise error protection (Šidàk, 1967; Huberty and Morris, 1989). Results were considered to be significant, when the Šidàk-corrected significance probability did not exceed αš = 0.004.
Regression model coefficients are presented in Table 1. Student's t-test was used to assess whether the linear or quadratic coefficients are significantly different from zero (which indicates that the ratings were affected by the timing manipulations). No tests were carried out with respect to the constant coefficients (intercept), because the EAG and SAM measures are greater than zero by design.
Table 1. Study A: Regression models for the relationships between Displacement Magnitude and the EAG and SAM response variables, separated by Timing Manipulation Method.
We observe that, for the Disturbance series (purple plots in Figure 3), the quadratic coefficients of the EAG scales (Entrainment, Enjoyment, and Irritation) differ significantly from zero. The corresponding linear coefficients are not significantly different from zero (this implies that the quadratic models are fairly symmetric about a Displacement Magnitude of 0 ms). Both, an increase of the displacements in negative (early) and positive (late) direction coincided with a decrease of the groove ratings. This manifests itself as a ∩-shaped plot in the case of Entrainment and Enjoyment, and as a ∪-shaped plot for Irritation. The quadratic models show that the Disturbance manipulation had significant effects on all three EAG response variables.
Figure 3. Study A: Effects of Displacement Magnitude (in seconds) on mean Entrainment, Enjoyment, and Irritation ratings, separated by microtiming manipulation method (Shift in green, Disturbance in purple). Error bars represent the standard error of the mean.
The largest effect was measured on Irritation (Cohen's f2 = 0.013), but this effect was very small according to Cohen's guidelines (Cohen, 1988). Effects on Entrainment (Cohen's f2 = 0.003) and Enjoyment (Cohen's f2 = 0.005) were significant, but even smaller. No significant effects were measured for the three SAM response variables. The effect of Disturbance on the SAM's Valence scale (p = 0.005) slightly exceeded the Šidàk-corrected significance limit of αš = 0.004; accordingly, it was not interpreted. The Shift manipulation (green plots in Figure 3) had no measurable effect on any of the response variables.
The results from the Disturbance series (purple plots in Figure 3) confirm the central finding of Frühauf et al. (2013). In their study, groove ratings declined as one instrument of the drum set was displaced relative to the quantized pattern of the other instruments. Similar to the Frühauf et al. study, our data shows a dose-response relationship: larger absolute displacements of the snare drum events in negative (early) or positive (late) direction are associated with lower groove ratings. In Senn et al. (2016), we argued that the response measured by Frühauf et al. (2013) might be explained using results from research on attention, which showed that irregular signals perceived against a regular background can easily be detected (Scerbo et al., 1986; Bregman, 1999; Parasuraman, 2000; Helton et al., 2005; Dalton et al., 2007; Winkler et al., 2009). We hypothesize that timing manipulations as those used in Frühauf et al. (2013) or the Disturbance pattern of Study A can be interpreted from the attention point of view: the manipulations introduce irregularities into an otherwise perfectly quantized and regular drum track, and the groove ratings decline as the listeners become more aware of these irregularities.
The largest effect size found by Frühauf et al. (2013) (η2 = 0.71) exceeds the effect sizes measured in the present study substantially. We can only guess why a similar timing manipulation led to responses which are very different in scale. One obvious reason is that Study A lost statistical power when data was discarded due to the methodological problem outlined above.
Another explanation considers musical content: the two studies used different rhythmic patterns. Frühauf et al. used a simple, generic rock beat. The displaced elements (bass drum or snare drum) appear on down- or backbeat positions that are crucial for the establishment of meter, and hence they are relatively exposed. In the Study A stimuli, the snare drum is much more busy and varied than in the drum pattern used by Frühauf et al. (2013). The snare drum events in the Study A stimuli occur on many different metric positions: they may appear on downbeats, but also on more obscure offbeat positions. In the Swing stimuli, the snare drum has the function of a “comping” instrument, which means that its contribution is rhythmically irregular, syncopated and arguably less relevant for the definition of the beat than the clear downbeat/backbeat pattern found in the Frühauf et al. stimuli (this flexibility of the snare drum is similar to the rhythmic flexibility of the piano, the prototypical comping instrument in the common jazz rhythm section, see Hodson, 2007, p. 33). Further, many snare drum events in the Study A stimuli were ghost notes (i.e., played very softly). Hypothetically, ghost notes might be less noticed than notes that are played loud. Finally, the stimuli of the present study featured an additional bass line which adds even more complexity. In summary, we hypothesize that the timing displacements in this study's stimuli have less impact in comparison to the displacements used by Frühauf et al., because they are less apparent due to the generally greater musical complexity and the fact that many displaced elements appear on less important metric positions, compared to the stimuli used in Frühauf et al.
We did not find any effect of the Shift manipulation on any of the response variables. This non-result complies with the findings of Butterfield (2010) who observed that the displacement of the bass and drums tracks against each other were not noticed by listeners. The non-effect seems to be consistent across both studies, even though the tasks set to the listeners were slightly different: Butterfield (2010) confronted his listeners with a perceptual discrimination task; listeners were prompted to decide which of the two instruments (bass or drums) played earlier, had a leading role, or played more assertively compared to the other instrument. In contrast, our Study A collected listeners' emotional reponses only. For neither of these listening reactions, the Shift manipulations had any effect, at least within the range of displacement magnitudes used in the two studies.
This non-effect challenges the explanation borrowed from attention research presented above: if listeners perceive irregularities against a regular background and react with lower groove ratings, why do they not react to the bass displacement against the regular drum beat? After all, the bass and the snare drum each represent one prominent layer, voice, or auditory stream (Bregman, 1999) of the musical fabric. One potentially crucial aspect is that the bass and the entire drum set are considered to be individual sound sources that are played by different musicians and occupy different locations in space. In contrast, the snare drum occupies approximately the same location as the other instruments of the drum set. Temporal displacements between separate sound sources are part of our everyday listening experience. In dry air, at 20°C, the traveling speed of sound is approximately 343 ms−1; sound takes 0.0029 s (or 2.9 ms) to travel a distance of 1 m between a sound source and a listener. Listening in a physical environment is always linked to relative time delays between events created by different sound sources, placed at different locations. From this point of view, Shift manipulations are potentially decoded by the listeners as spatial information, not as an irregular foreground contrasting with a regular background. This might explain why the Shift manipulations did not have an effect on any of the response variables. Inverting the argument, we may claim that the Disturbance manipulation indeed did have an effect, because the snare drum and the other instruments of the drum set are assumed to be located at the same place. Hence, timing discrepancies cannot be traced back to different localizations of the sounds, instead they must originate in irregularities within the performance of one musician.
Contrasting with these results, Matsushita and Nomura (2016), in their first experiment, measured a significant effect of the time Shift between bass and drums. The effect might be explained by the sheer magnitude of the displacements in their experiment: at maximum asynchrony, the instruments' onsets were 62.50 ms apart, which is more than double the magnitude used in the other studies. These large displacements represent a delay time corresponding to a distance of 21.4 m. Such a large distance between musicians is not realistic in a duo performance context. So, in this extreme case, listeners' spatial interpretation of the time delays potentially breaks down, and they perceive the delays as poor synchronization.
To summarize, we used an argument about the (imagined) spatial arrangement of instruments and their players to claim that the Shift manipulations may be decoded by listeners as spatial information, which does not trigger an emotional response, whereas the Disturbance manipulations are potentially heard as imprecise playing of one of the performers, which leads to a negative emotional response.
3. Study B: In Search of a Context-Independent Measure for Microtiming Magnitude
In Senn et al. (2016) we reported that listeners' emotional responses were similar for swing and funk when the originally performed microtiming patterns were scaled by the same percentages. This was a surprising result, because the microtiming deviations (measured in milliseconds) of the swing performance were more largely spread than the deviations of the funk performance, and this difference was accentuated when the microtiming deviations were upscaled. Do listeners have an implicit knowledge which microtiming magnitudes are appropriate in each style? In Study B, instead of accepting such an essentialist explanation, we will try to identify moderating factors that bridge the differences, using the EAG response data from Senn et al. (2016).
Three different measures of microtiming magnitude will be defined: the Standard Timing Deviation (STD) estimates the mean microtiming deviation per note onset (a bass tone, a stroke on a drum etc.) in milliseconds using the Root Mean Squared Error method. The Tempo-adjusted Standard Timing Deviation measures mean deviation as a proportion of beat duration. And the Density-adjusted Standard Timing Deviation measures it as a proportion of the mean IOI between neighboring rhythmic events.
The two adjusted measures translate the core result of Friberg and Sundberg's (1995) experiment into the domain of more complex musical stimuli: for the monotonic, isochronous sequences of their experiment, a change of IOIs can equivalently be understood as a manipulation of tempo or of rhythmic density. For more complex musical objects, these two aspects need to be separated. A complex pattern might move at a slow tempo, but still be rhythmically dense, and vice versa.
The Tempo-adjusted STD implements tempo as a moderator variable (see Baron and Kenny, 1986; Cohen, 2003), which, in Western popular music, is represented by the periodicity of the beat. In contrast, the Density-adjusted STD considers how frequently a listener obtains any kind of rhythmic information. The two measures lead to different values when applied to this study's swing and funk stimuli: the swing example has higher tempo (150 bpm) than the funk example (100 bpm), hence the Tempo-adjusted STD augments the microtiming magnitude of the swing example relative to the funk example. But the rhythmic density of the swing example is smaller than the density of the funk example, because the smallest subdivision in swing are eighth notes, whereas the funk example has an underlying sixteenth note pulse. Hence Density-adjusted STD will accentuate the microtiming magnitude of the funk example relative to the swing example.
In a second step, we will analyse listeners' emotional responses to the swing and funk stimuli as a function of the three different microtiming measures. If any of the measures successfully aligns listeners' responses to microtiming in both the swing or the funk contexts, this measure potentially can be used to uniformly quantify microtiming across different musical situations, and the adjusting factor can be understood as a unifying moderator variable.
3.1. Three Versions of the Standard Timing Deviation
The three methods for calculating summary timing deviation measures are differently scaled variants of the Root Mean Squared Error or RMSE, which is widely used for estimating the spread of a stochastic variable around an expected value.
The simple Standard Timing Deviation (STD) is measured in seconds, and it is calculated as follows:
where n is the number of note onsets in the musical passage, ti is the exact time of the ith note onset, and is the corresponding quantized time point on the metronomic grid (or expected onset time). For the 20 s swing passage that is presented in Figure 1 (with the originally performed microtiming deviations in milliseconds given as numeric values next to each note), we calculate an STD value of 0.0272 s (27.2 ms). For the funk passage showed in Figure 2 we calculate a smaller value of 0.0157 s (15.7 ms).
The Tempo-adjusted Standard Timing Deviation was already introduced in Senn et al. (2016). Based on the findings of Friberg and Sundberg (1995) and Ehrlé and Samson (2005), we hypothesized that listeners would be more sensitive to microtiming deviations at higher tempi than at lower tempi. The Tempo-adjusted STD is measured as a proportion of the beat, and it is calculated as:
where bpm is the tempo of the music in beats per minute, and all other variables are the same as above. A comparison of Equations (1) and (2) shows that the tempo-adjustment is a scaling of the simple Standard Timing Deviation by the constant factor bpm/60, which represents the number of beats per second. For the swing passage with the originally performed microtiming, the Tempo-adjusted STD is 0.0681 (6.81% of the duration of a beat), whereas for the original funk clip, we obtain a value of 0.0262 (2.62% of the beat).
Before defining the Density-adjusted Standard Timing Deviation, we first define rhythmic density ρR as follows:
where E is the number of distinct events (a distinct event is a metronomic grid position on which at least one note onset occurs). Further, and are the expected times of the first and the last event, respectively. Rhythmic density ρR measures the mean number of distinct events per second, and it is the reciprocal of the mean inter-onset-interval (). The Density-adjusted Standard Timing Deviation is then calculated as
and it measures timing deviation as a proportion of . The swing passage with originally performed microtiming shows a Density-adjusted STD value of 0.1064 (or 10.64% of the ), whereas the original funk performance has a value 0.0911 (or 9.11% of the mean inter-onset-interval).
3.2. Materials and Methods
3.2.1. Stimuli and Timing Manipulations
The stimuli for Study B were derived from the same two swing and funk recordings that were used in Study A. The timing was manipulated by scaling the originally performed microtiming deviations (see transcriptions and microtiming deviations given in Figure 1, 2) relative to the metronomic grid. The scaling had eleven different levels: −100, −80, −60, −40, −20, 0, +20, +40, +60, +80, +100%. On the 0% level, the microtiming deviations from the metronomic grid were exactly as in the originally recorded performance. The +100% level upscaled the original deviation of every note to double magnitude. And on the −100% level, the deviation of every note was downscaled so that all events occurred on the metronomic grid, and the music was perfectly quantized (with the swing example showing an eighth-note swing ratio of 2.66). The same procedure was applied to the examples from both styles, swing and funk, based on the microtiming deviations occurring in the original performance. The upscaling exaggerated the microtiming profiles of the original performances, whereas the downscaling flattened these profiles (for more details on the timing manipulations, see Kilchenmann and Senn, 2015; Senn et al., 2016). The stimuli for Study B can be downloaded from the Supplemental Material section of Kilchenmann and Senn (2015).
3.2.2. Participants, Procedure, Psychometric Measures, and Statistical Design
A total of n = 160 persons participated in the experiment, which was carried out at the Lucerne University of Applied Sciences and Arts. Seventy nine participants were considered to be music Experts, as they had either obtained a professional music degree (musicians or music educators), or were enrolled in programs to earn such a degree. The remaining 81 participants did not meet these criteria and were considered to be musical Non-Experts. Most of the participants were either affiliated with the Lucerne University of Applied Sciences and Arts or with Lucerne University. The sample consisted of 82 female and 78 male participants, their ages ranged from 18 to 47 years (, s = 4.3), and all participants were fluent German speakers. The majority of participants was recruited via emails and class visits, a few were invited through personal communication channels.
The experimental procedure of Study B was identical to the procedure employed in Study A; it was also approved by the Ethics Committee of the Canton of Lucerne. Participants performed the listening test alone in a quiet office. They followed on-screen instructions, listened to the stimuli through studio headphones and filled the questionnaires by mouseclick. Each participant heard all stimuli of one style, and the sequence of the presentation was randomized. The participants rated the stimuli using the Emotional Assessment of Groove (EAG) and Self-Assessment Manikin (SAM) questionnaires. Only the three EAG scales (Entrainment, Enjoyment, and Irritation) will be considered in the subsequent analyses. These three scales have been shown to record participants' emotional responses to microtiming manipulations quite consistently (see Senn et al., 2016 and Study A).
Statistical analyses were carried out separately for each of nine combinations of measurement method (STD, Tempo-adjusted STD, Tempo-adjusted STD) and outcome variable (Entrainment, Enjoyment, and Irritation). In each combination, regression models were fitted to the data, separated by Style (Swing, Funk), with the microtiming measurement as predictor, and listeners' emotional response as outcome variable. Quadratic models were used if they had a significantly better fit in both styles than alternative first-order linear models. Finally, model coefficients were compared across styles in order to determine whether the models were significantly different.
The overall significance level was set to α = 0.05. No correction was applied for familywise error protection, because Type I errors are no serious concern in Study B: the focus lies on non-significant test results, because they indicate that there is little evidence against the null hypothesis of no difference between listener reactions to Swing or Funk stimuli. Non-significant test results inform us that the respective microtiming measure predicts invariant listener responses across the two style contexts; hence it is potentially a generally applicable measure for microtiming, and the adjusting variable is a mediator.
Figure 4 plots the three EAG response variables (Entrainment, Enjoyment, and Irritation) against the three summary microtiming measures (STD, Tempo-adjusted STD, and Density-adjusted STD) for each of the stimuli. Each row of diagrams refers to the same response variable, whereas each column of diagrams refers to the same timing deviation measure. Participants' responses to the eleven Swing stimuli are plotted in red; responses to the Funk stimuli are plotted in blue. In each of the nine plots, the stimuli with completely quantized timing in either style are shown to the far left and marked with a vertical black line. This position corresponds to a magnitude of zero on all three Standard Timing Deviation scales. The microtiming magnitudes of the original performances are marked with a red (Swing) or a blue (Funk) vertical line. These values differ for each Style and for each of the three STD measures. The responses to the stimuli with doubled microtiming deviations are furthest to the right in each plot.
Figure 4. Study B: Mean EAG variables (Entrainment, Enjoyment, Irritation) as a function of three microtiming magnitude measures, separated by Style (Swing, Funk). The Standard Timing Deviation measures microtiming magnitude in seconds (s), the Tempo-adjusted Standard Timing Deviation measures it as a proportion of mean beat duration (B), and the Density-adjusted Standard Timing Deviation as a proportion of mean IOI. Error bars represent the standard error of the mean.
As a general response pattern, Entrainment and Enjoyment ratings are high for small timing deviation values and decline with larger values; Irritation inverts this pattern (for a more detailed report, see Senn et al., 2016). We are now interested to study, to what extent response patterns differ or align across styles when the EAG ratings are represented as functions of either of the three timing deviation measures.
In order to compare the responses across styles for the different combinations of timing deviation measurements and EAG scales, either linear or quadratic regression models were fitted to the data. With respect to Entrainment, a quadratic model fits the Swing data better than a linear model [F(1, 957) = 5.775, p = 0.016], but an additional quadratic term does not significantly improve the fit of the Funk data [F(1, 945) = 2.123, p = 0.145]. Consequently only linear models were used to analyse the Entrainment data of both Styles. With respect to Enjoyment, the situation is similar: the quadratic term is significantly different from zero for Swing [F(1, 957) = 8.690, p = 0.003], but not for Funk [F(1, 945) = 1.060, p = 0.303], hence only linear models were fitted to the data. For the Irritation data, quadratic models have a significantly better fit than linear models for both Styles [Swing: F(1, 957) = 19.791, p < 0.001; Funk: F(1, 945) = 20.690, p < 0.001], hence quadratic models were used to compare the Irritation responses.
The model coefficients using the simple Standard Timing Deviation as predictor variable are presented in Table 2; the models are visualized as sloping straight lines (Entrainment, Enjoyment), or as parabolae (Irritation) in the left column of Figure 4. The linear models predict that Entrainment and Enjoyment are negatively related to Standard Timing Deviation for both Swing and Funk, as expected. As the Standard Timing Deviation increases, the Entrainment and Enjoyment ratings decline, indicating that the groove experience deteriorates with higher Standard Timing Deviation. For Irritation, the quadratic term was significantly greater than zero with respect to both the Swing and Funk datasets. The models predict that listeners' Irritation grows increasingly with higher values of Standard Timing Deviation.
Table 2. Regression models for the relationship between Standard Timing Deviation and Entrainment, Enjoyment, and Irritation for Swing, Funk, and the difference between Swing and Funk.
Comparative tests for the difference between Swing and Funk models are also presented in Table 2 (Swing – Funk). For Entrainment, the slope of the Funk linear model was significantly steeper than the slope of the Swing model. For Enjoyment, this difference was only nominal. For Irritation the quadratic term of the Funk model was significantly greater than the equivalent term of the Swing model. This means that the simple Standard Timing Deviation is sensitive to the style context: the models predict that listeners react more strongly to microtiming in Funk compared to Swing, if it is measured using the Standard Timing Deviation.
Table 3 presents a similar analysis, but this time using the Tempo-adjusted Standard Timing Deviation as a predictor variable. This analysis corresponds to the plots in the middle column of Figure 4. We observe that the Swing and Funk regression models are significantly different from each other for all three EAG response variables (Entrainment, Enjoyment, Irritation), hence the Tempo-adjusted Standard Timing Deviation is also sensitive to the style context.
Table 3. Regression models for the relationship between Tempo-Adjusted STD and Entrainment, Enjoyment, and Irritation for Swing, Funk, and the difference between Swing and Funk.
Table 4 finally analyses the EAG responses with respect to the Density-adjusted Standard Timing Deviation as predictor variable. We observe that the differences between the models fitted to the Swing and Funk data are statistically insignificant. The models align quite closely, as can be seen in the rightmost column of Figure 4. This suggests that the Density-adjusted Standard Timing Deviation measure is relatively insensitive to the differences between the Swing and the Funk examples of Study B.
Table 4. Regression models for the relationship between Density-Adjusted STD and Entrainment, Enjoyment, and Irritation for Swing, Funk, and the difference between Swing and Funk.
If listener reactions were expressed as a function of the Standard Timing Deviation or of the Tempo-adjusted Standard Timing Deviation, the Style variable had a significant effect. In contrast, Style became irrelevant, when microtiming magnitudes were measured as Density-adjusted Standard Timing Deviation. The uniformity of responses across the two Styles suggests that the Density-adjusted Standard Timing Deviation is a more context-insensitive measure for the subjectively experienced microtiming magnitude, compared to the two other measures. Rhythmic density, potentially, is a moderating factor for listeners' emotional responses to microtiming.
Note that the originally performed microtiming magnitudes also aligned, when they were measured as Density-adjusted STD: in both the Swing and Funk examples, the originally performed mean microtiming magnitude amounted to approximately 10% of the music's mean IOI. The density-adjustment suggests that the musicians' performances show consistent microtiming across the two styles.
The main finding of Study B can be related to previous research:
1. The result agrees with the perception-oriented findings of Friberg and Sundberg (1995): similar to the just-noticeable differences, listeners' emotional responses to microtiming deviations appear to depend on rhythmic density or, reciprocally, on the mean time interval between subsequent events. Thus, they seem to obey Weber-Fechner's law: when time intervals between events are small, listeners are affected by small microtiming variations. But if intervals are large, it takes greater microtiming variations to have an effect on listeners.
2. The result is compatible with recent findings on sensori-motor synchronization: Madison (2014) found that tapping along with a musical stimulus was more accurate, when a higher number of metrical levels was present in the acoustic stimulus (for example eighth notes in addition to quarter notes). Adding metrical levels is equivalent to augmenting the rhythmic density without altering the tempo. We may assume that the precision of sensori-motor synchronization depends on tappers' perceptual sensitivity to timing discrepancies as studied by Friberg and Sundberg (1995) (only people who perceive that they are tapping off the beat are able to adapt their performance). Similarly, the emotional reactions to microtiming as measured by the EAG may also depend on listeners' sensitivity to timing discrepancies. The results of both Madison (2014) and Study B are presumably rooted in the same cognitive substrate that governs the JND of time perception (Friberg and Sundberg, 1995).
3. A further connection can be drawn to the Theory of Attentional Dynamics (Large and Jones, 1999; Jones et al., 2006) which discusses models of attention and expectation in the perception of periodically recurring events. This theory models the expected arrival time of an event as a bell-shaped probability distribution, which is updated every cycle in a quasi Bayesian way. The spread of the distribution (and hence the expected temporal variability of events) is thought to depend on the variability of past events and on the mean IOI between these events (see Large and Jones, 1999, p. 132). Hence, the Theory of Attentional Dynamics appears to associate the variables in a similar way as Friberg and Sundberg (1995) and the present study, but it adds a procedural perspective.
In summary, the idea that rhythmic density (but not tempo) might be a relevant moderating factor for listeners' emotional response to microtiming deviations seems to be compatible with theories on auditory perception, sensori-motor synchronization and dynamic attending. Nevertheless, our study's empirical evidence, based on two performances only, is too circumstantial to warrant a general claim. More research is necessary in order to study whether the scope of the result expands beyond these two particular recordings, beyond the playing of these two particular musicians at this particular day, and beyond the swing and funk styles. For the data at hand, however, the Density-adjusted Standard Timing Deviation appears to be our best guess of a metric that allows to compare listeners' response behavior across the different musical contexts.
4. Study C: Comparing the Effect of Lab- and Performance-Generated Microtiming Patterns on Music Experts and Non-Experts
The previous section presented some evidence that the Density-adjusted Standard Timing Deviation aligns listener responses to microtiming deviations across different musical contexts. This alleged invariance is not more than a claim at this point. But if confirmed by further research, it might provide a welcome common ground for the comparison of microtiming effects arising in different situations and contexts.
In Study C, we probe into the potential of such comparisons: we hypothesize that listeners respond differently to the invariant, lab-generated Shift and Disturbance patterns (Study A) compared to the variable microtiming pattern that arose in the original Performances (Study B). The rationale behind this claim is that we expect listeners to be more accustomed to Performance microtiming patterns than to Shift and Disturbance patterns. And we also hypothesize that Expert listeners react more strongly to microtiming deviations, compared to Non-Expert listeners, due to the Experts' training and refined auditory perception. In order to test these hypotheses, we will fit regression models to listeners' EAG ratings, using the Density-adjusted STD as a common predictor variable.
No new listener responses were collected for Study C. Instead, the EAG groove ratings from Studies A and B were re-analyzed in order to test the hypotheses raised above. The Density-adjusted STD value was calculated for each stimulus, using Equation (4). These values are presented in Table 5.
Table 5. Density-Adjusted STD values for the stimuli of the Shift, Disturbance and Performance microtiming patterns.
We observe that, in the Shift and Disturbance series, negative and positive displacements of the same magnitude are projected onto the same Density-adjusted STD value for both the Shift and Irregularity series (this follows from the fact that negative and positive values are positive when squared). We also see that the originally performed Swing and Funk microtiming patterns (0%) show considerably larger Density-adjusted STDs than the largest values associated with the Shift and Disturbance series. This discrepancy is particularly striking for Swing. In order to compare data with an approximately similar range on the predictor variable, only listener responses to stimuli with originally performed (0%) and downscaled (−20, −40, −60, −80, −100%) microtiming were used for the regression analysis; the data corresponding to stimuli with exaggerated (upscaled) performance microtiming were not included.
The regression analyses test whether linear models fitted to the Shift and Disturbance listener response data (both from Study A) differ significantly from linear models fitted to the Performance data. The stimuli from the two Styles (Funk, Swing) will be analyzed separately. Similarly, the two Expertise groups (Expert, Non-Expert) will each be modeled on their own. With three EAG outcome variables (Entrainment, Enjoyment, Irritation) and four Expertise/Style combinations, twelve regression models will be fitted in parallel. The overall significance level is set to α = 0.05. There were twelve parallel comparisons, hence the Šidàk-corrected significance level was set to αš = 0.004.
Figure 5 plots the mean Irritation ratings against the Density-adjusted STD for the different stimuli series Shift (green), Disturbance (purple), and Performance (blue or red). The responses to the Funk stimuli with the different scalings of the original Performance microtiming pattern (from Study B) are shown in blue on the left, the Swing stimuli are presented in red on the right.
Figure 5. Study C: Mean Irritation as a function of the Density-adjusted Standard Timing Deviation (proportion of mean IOI) across different Style/Expertise combinations for the Disturbance (purple) and Shift (green) manipulation patterns and the Performance pattern (blue or red) scaled from quantized to original magnitude. Error bars represent the standard error of the mean.
Test results for the Irritation response data are presented in Table 6. The Shift – Performance tests analyse whether the intercepts or the slopes of simple linear regression models fitted to the Shift series differ significantly from the models of the corresponding Performance series. Similarly, the Disturbance–Performance tests detect differences between the Disturbance and corresponding Performance models.
Table 6 shows further that, for music Experts listening to Funk, the slope of the Irritation linear regression model fitted to the series with Disturbance pattern is significantly greater than the slope of the model fitted to the respective Performance pattern. We can state that music experts' Irritation increased significantly stronger, when larger irregularities were introduced into the stimuli by displacing the snare drum events, compared to when the originally performed microtiming pattern magnitude was increased. In other words: the few fixed snare displacements against a quantized background were more irritating to expert listeners than the flexible timing displacements occurring all over the actual performances.
The Shift of the bass track against the drums track did not have such an effect. Tests applied to the Non-Experts and Swing data were not significant; similarly, no effects were measured with respect to the Entrainment and Enjoyment data (tables and figures are omitted).
The question of whether and how different microtiming patterns affect the emotional reactions of listeners cannot be conclusively answered from the data available. We found some evidence that Expert listeners reacted more irritatedly to the fixed snare displacements of the Disturbance series in Funk compared to scaled microtiming variations of the original performances. But this result is isolated, and it needs to be corroborated by further research.
Nevertheless, the data offers clear pointers why the question at hand could not be fully answered: we observe that the independent variable (Density-adjusted STD) of the Disturbance and Shift pattern series showed considerably smaller ranges in comparison to the scaled original performance patterns. Under the assumption that microtiming magnitude is adequately measured by Density-adjusted STD, the microtiming deviations of the Disturbance and Shift series would need to be expanded in order to compare the patterns on an equal footing. Specifically, the maximum displacements in Funk would have to be augmented to 35 ms for the Disturbance pattern, and the Shift would need to be augmented to 34 ms in order to match at the Density-adjusted STD value of the original Funk performance. The deviations within the Swing stimuli would need to be augmented to 69 ms (Disturbance) and 59 ms (Shift) in order to attain the Density-adjusted STD value of the original performance (note that these magnitudes are fairly close to those used by Matsushita and Nomura (2016)). A new experiment could clarify the effect of different microtiming patterns by adjusting the Density-adjusted STD to comparable magnitudes.
This paper's Study A replicated results from previous research. It showed that, on one hand, the mutual displacement of the bass track against the drums track by a magnitude up to 24 ms (Shift) did not have a significant effect on listeners' groove experiences (thus solidifying the main result of Butterfield, 2010). On the other hand, the study showed that displacing the snare drum track alone against the other tracks (Disturbance) affected listeners' groove experience negatively (thus it confirmed a core finding of Frühauf et al., 2013). Overall, we could confirm that not only the magnitude of microtiming deviations seems to matter, but also which layers of a musical fabric were affected: displacements between instrumental parts played by the same musician had a negative effect, whereas displacements between parts played by different musicians had no effect.
The effect of the Disturbance manipulation measured in Study A was considerably smaller than the effect found by Frühauf et al. (2013). This may have had several reasons: firstly, Study A had a flaw in the experimental setup that invalidated the responses to the quantized stimuli. This loss of data affected the statistical power of the experiment adversely. Secondly, the musical patterns of the stimuli in Study A differed considerably from those in Frühauf et al. (2013). The latter used a simple, straightforward rock music drum pattern, in which the snare drum played the quintessential backbeat and nothing else. In the present paper, both the swing and funk stimuli present a more complicated pattern, where the snare drum may appear on different, more or less metrically important positions. Attention research claims that irregular signals are easier to detect, when they are perceived against a regular background (Scerbo et al., 1986; Bregman, 1999; Parasuraman, 2000; Helton et al., 2005; Dalton et al., 2007; Winkler et al., 2009), compared to an irregular background. The pattern presented in Frühauf et al. (2013) represents a regular background, whereas the music used in this paper's Study A are rhythmically far more complicated and irregular, and this might have affected both the detectability and emotional impact of the microtiming displacements.
Study B explored different ways of measuring the magnitude of microtiming deviations in music. Using data from an earlier study (Senn et al., 2016), it showed that emotional listener reactions aligned with each other across two select musical styles (Funk and Swing), when microtiming magnitude was measured as a proportion of the mean IOI, and rhythmic density is accounted for as a moderating factor. The result suggests that the Density-adjusted STD is potentially better suited than the simple STD or the Tempo-adjusted STD to measure and compare the magnitude of microtiming phenomena across a variety of musical situations. This finding resonates with results from research on just-noticeable differences in auditory perception (Friberg and Sundberg, 1995), sensori-motor synchronization (Madison, 2014) and attentional dynamics (Jones et al., 2006). Further studies are necessary to establish whether the result expands to other musical contexts beyond the two situations investigated in this paper.
Finally, Study C probed into the effects of different microtiming patterns on listeners' emotional response, using the Density-adjusted STD as common measure of microtiming magnitude. This inquiry yielded one marginal result only: it showed that music expert listeners reacted with more Irritation when the snare drum was displaced against a quantized background, compared to the widely distributed microtiming patterns present in the originally recorded performances. A collateral result is perhaps of greater importance: Study C showed that the artificially introduced microtiming deviations (Shift, Disturbance) were of far smaller magnitude than the displacements found in the original performances. This suggests that, in future research, the magnitudes of lab-created and performance-generated microtiming patterns need to be matched in order to be comparable.
Overlooking the corpus of groove studies concerned with microtiming, we may summarize that this branch of research has surprisingly few results to show for its efforts. This is what we know so far: stimuli with quantized timing receive high groove ratings, at least in the context of an experiment. We also know that groove ratings deteriorate, as microtiming deviations are expanded (Davies et al., 2013; Frühauf et al., 2013; Matsushita and Nomura, 2016; Senn et al., 2016). We further obtained some evidence that the groove ratings of stimuli with microtiming that arises in professional performance are on par with those elicited by stimuli with quantized timing (Senn et al., 2016) and that it is not necessarily the quantized stimuli that motivate the most intense body movement in listeners (Kilchenmann and Senn, 2015). Finally, Hofmann et al. (2017) suggested that tight microtiming patterns are preferred by listeners, compared to larger microtiming magnitudes.
These results offer little support to the claim of the Theory of Participatory Discrepancies that microtiming deviations are essential for groove. Rather, it led scholars to the opposite conclusion, namely that music should be played with as little microtiming as possible in order to have high groove—this opinion has been most prominently voiced by Merker (2014). This conclusion, however, is at odds with the firm belief of many musicians that microtemporal aspects are crucial for groove (Berliner, 1994; Monson, 1996; Doffman, 2008) and with the findings of Hove et al. (2007) that the presence of certain kinds of microtiming improves rhythmic precision in musicians.
In a recent article, Witek pointed out with respect to microtiming that “it seems to matter greatly how these rhythmic nuances are implemented,” and she observed that “the conditions under which microtiming is effective have been difficult to recreate in a laboratory setting” (Witek, 2016, p. 16, emphasis in the original). Taking into account relevant moderating factors bridging between “conditions” and using patterns from expert performance in empirical research may allow to reach beyond the lab situation and better capture the groove phenomenon. Up to now, most experimental studies investigated the effect fixed microtiming patterns, and manipulated them by scaling the magnitude of the deviations. But the effect of systematically varying the patterning of microtiming deviations while retaining the magnitude of the deviations has not yet been sufficiently approached. The methodological advantage of varying magnitude as an experimental variable consists in the fact that, being a quantity, magnitude can be manipulated along one single dimension. Varying patterns is inherently multidimensional, and it is difficult to imagine how systematic manipulations can be achieved.
Another yet unchartered territory in groove studies is related to the variability of microtiming patterns across the time of a performance. In the past, average microtiming variation profiles have been extracted from performed music (see examples in Fujii et al., 2011; Kilchenmann and Senn, 2011, 2015; Naveda et al., 2011; Hellmer and Madison, 2015; Hofmann et al., 2017). These patterns may well represent a systematic aspect of microtiming in a certain style or context. But they do not take the procedural aspect of music performance into account: the development of the music in time and the rhythmic interaction between players. According to Keil (1966, 1987, 1995, 2010) this procedural aspect is of great importance. Large's and Jones' Theory of Dynamic Attending (Large and Jones, 1999; Jones et al., 2006) might provide a solid conceptual basis for tackling process-related aspects of musical performance.
In recent years, research has moved on to discuss other musical aspects that promise to be relevant for groove, besides microtiming. Beat salience, event density and pulse clarity have been identified as possible sources for (or at least correlates of) groove (Madison et al., 2011; Stupacher et al., 2016). Several studies have shown that syncopation is associated with the experience of groove (Madison and Sioros, 2014; Sioros et al., 2014; Witek et al., 2014) and with body movement (Witek et al., 2017). The groove qualities of rhythmic patterns have also been discussed, albeit not yet empirically (Zbikowski, 2004; Danielsen, 2006; Witek, 2016). The diversification of groove studies, to include other factors than microtiming, is a fruitful and necessary development. There is currently no shared doctrine among scholars whether microtiming is relevant or not, but everybody will agree that microtiming cannot be the only relevant aspect for groove, and that there are innumerable other routes to explore.
This study was carried out in accordance with the recommendations of the Ethics Commission of the Canton of Lucerne. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee of the Canton of Lucerne.
Conceived and designed the experiments, wrote the paper, and designed the EAG questionnaire: OS, LK, RvG, and CB. Conducted the studio recording session and performed the experiments: LK. Validated the EAG questionnaire: RvG. Analyzed the data: OS.
This research was part of the project “The Relevance of Participatory Discrepancies for the Perception of Groove in Jazz and Funk,” supported by the Swiss National Science Foundation (grant 100012L 137794 to OS) and the Deutsche Forschungsgemeinschaft (grant BU 2259/1-1 to CB).
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2017.01709/full#supplementary-material
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Keywords: groove, microtiming, emotions, rhythmic density, popular music
Citation: Senn O, Bullerjahn C, Kilchenmann L and von Georgi R (2017) Rhythmic Density Affects Listeners' Emotional Response to Microtiming. Front. Psychol. 8:1709. doi: 10.3389/fpsyg.2017.01709
Received: 26 April 2017; Accepted: 19 September 2017;
Published: 12 October 2017.
Edited by:Aaron Williamon, Royal College of Music, United Kingdom
Reviewed by:Renee Timmers, University of Sheffield, United Kingdom
Mats B. Küssner, Humboldt-Universität zu Berlin, Germany
Copyright © 2017 Senn, Bullerjahn, Kilchenmann and von Georgi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Olivier Senn, firstname.lastname@example.org