<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Signal Processing | Signal Processing Theory section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/signal-processing/sections/signal-processing-theory</link>
        <description>RSS Feed for Signal Processing Theory section in the Frontiers in Signal Processing journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-06T00:37:11.462+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1567926</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1567926</link>
        <title><![CDATA[Deep learning-based GNSS composite jamming detection and recognition technology]]></title>
        <pubdate>2025-04-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chenxuan Liu</author><author>Binbin Ren</author><author>Yuchen Xie</author><author>Feiqiang Chen</author>
        <description><![CDATA[As the interference environment of Global Navigation Satellite Systems (GNSS) becomes increasingly complex and diverse, real-time and precise interference detection and identification technologies are crucial for enhancing the anti-interference capabilities of receivers. However, most existing interference detection and identification methods focus on single interference types, with limited research on composite interference and a lack of quantitative conclusions. Therefore, this study investigates composite interference detection and identification techniques using deep learning methods, improving the system’s capability to detect and identify composite interference. This paper first constructs single interference model and composite interference model, proposes three signal preprocessing methods, and generates corresponding image datasets. Subsequently, the interference detection and identification performance under different signal preprocessing methods is analyzed using the ResNet-18 deep learning neural network. The optimal signal preprocessing method is identified, and quantitative conclusions are obtained. Finally, a lightweight network, LcxNet-Fusion, is designed, which significantly reduces the number of network parameters and forward processing time while maintaining an acceptable level of accuracy reduction. Results show that among the time-frequency 2D diagrams, power spectral diagrams, and histograms generated by signal preprocessing, the time-frequency diagram yields the best detection and identification performance. When the detection rate reaches 90%, the jamming-to-noise ratio (JNR) sensitivity of the time-frequency diagram is −20 dB; when the identification rate reaches 90%, the JNR sensitivity of the time-frequency diagram is −13 dB. On the Tesla V100 GPU, the LcxNet-Fusion network has 24.32 MB parameters, an 43% reduction compared to the ResNet-18 network, with a forward processing time of 1.25 s, reducing by 15%. This work holds promising prospects in the field of interference detection and identification for GNSS systems under complex electromagnetic environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1582043</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1582043</link>
        <title><![CDATA[Theoretical and experimental results on time-series representations of polynomials]]></title>
        <pubdate>2025-04-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Asoke K. Nandi</author>
        <description><![CDATA[This study considers time-series representations of polynomials. Often in data modelling and many other applications, accurate estimations of the degree of a polynomial, of the noise standard deviation, and of the coefficient of the highest degree of a polynomial are useful in detection, estimation, and prediction. The major contributions of this paper can be found in the original research offering novel theoretical and experimental results. The theoretical results include an alternative proof of the qth order AR time-series representation, with a constant, of a polynomial of degree q, an alternative proof of the (q + 1)-th order AR time-series representation, without a constant, of a polynomial of degree q, as well as generalized equations (valid for a polynomial of an arbitrary degree) for reduced variance estimation of the polynomial coefficient corresponding to the highest degree. The experimental investigations are the most comprehensive so far, in that they use well over 35 times more realisations than before, use a greater variety of noisy data (Gaussian, Uniform, and Exponential noise), and use a larger range of polynomial degrees as well as of noise standard deviations than before. Experimental results on estimations of the degree of a polynomial, of the noise standard deviation, and of the polynomial coefficient corresponding to the highest degree using seven methods (AIC, AICc, GIC, BIC, Chi-square, F-distribution, and PTS2) are presented. Results indicate clearly that PTS2 performs the best.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1518558</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1518558</link>
        <title><![CDATA[ICEEMDAN–VMD denoising method for enhanced magnetic memory detection signal of micro-defects]]></title>
        <pubdate>2025-02-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shouhong Ji</author><author>Jie Yan</author><author>Yang Liu</author><author>Guojun He</author>
        <description><![CDATA[Ferromagnetic materials are extensively utilized in industrial settings where the early detection and repair of defects is paramount for ensuring industrial safety. During the enhanced magnetic memory detection of micro-defects, many interference signals appear in the detection signal, which makes it difficult to accurately extract the characteristics of the micro-defect signals, significantly affecting detection effectiveness. When improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed independently for signal denoising, the noise and feature signals of the transition components are retained or removed. When variational mode decomposition (VMD) is employed independently for signal denoising, the denoising effect is restricted because of the difficulty in determining the penalty factor α and the number of decomposition layers m. To solve these problems, a denoising method for enhanced magnetic memory detection signals based on ICEEMDAN and VMD, called ICEEMDAN–VMD, is proposed in this paper. First, a comprehensive index (CI) combining information entropy (IE) and the correlation coefficient R is proposed, then the signal components obtained by performing decomposition with the ICEEMDAN method are divided into noise-dominant components, transition components, and useful signal components based on the CI. Subsequently, VMD is employed to perform secondary decomposition on the transition components obtained from the ICEEMDAN method and calculate the correlation coefficients. Ultimately, the optimal VMD components and useful signal components obtained by the ICEEMDAN method are selected for signal reconstruction to obtain a denoised signal. To validate the effectiveness of the proposed method, the denoising effects of the ICEEMDAN–VMD, ICEEMDAN, and VMD methods were compared based on the signal-to-noise ratio (SNR) and fuzzy entropy (FE). The comparison indicated that the ICEEMDAN–VMD denoising method significantly enhanced the denoising effect, and the SNRs of the components of the magnetic field signal could be increased by up to 69.426%. The SNR of each gradient component of the magnetic field signal could be improved by up to ten times, and the FEs of the signal components and their corresponding gradient components could be reduced by 24.198%–81.011%, respectively.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1357995</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1357995</link>
        <title><![CDATA[Dynamic phasor measurement algorithm based on high-precision time synchronization]]></title>
        <pubdate>2024-02-29T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Jie Zhang</author><author>Fuxin Li</author><author>Zhengwei Chang</author><author>Chunhua Hu</author><author>Chun Liu</author><author>Sihao Tang</author>
        <description><![CDATA[Ensuring the swift and precise tracking of power system signal parameters, especially the frequency, is imperative for the secure and stable operation of power grids. In instances of faults within the distribution network, abrupt changes in frequency may occur, presenting a challenge for existing algorithms that struggle to effectively track such signal variations. Addressing the need for enhanced performance in the face of frequency mutations, this paper introduces an innovative approach—the Covariance Reconstruction Extended Kalman Filter (CREKF) algorithm. Initially, the dynamic signal model of electric power is meticulously analyzed, establishing a dynamic signal relationship based on high-precision time source sampling tailored to the signal model’s characteristics. Subsequently, the filter gain, covariance matrix, and variance iteration equation are determined based on the signal relationship among three sampling points. In a final step, recognizing the impact of the covariance matrix on algorithmic tracking ability, the paper proposes a covariance matrix reset mechanism utilizing hysteresis induced by output errors. Through extensive verification with simulated signals, the results conclusively demonstrate that the CREKF algorithm exhibits superior measurement accuracy and accelerated tracking speed when confronted with mutating signals.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1334782</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1334782</link>
        <title><![CDATA[Corrigendum: Spread spectrum modulation recognition based on phase diagram entropy]]></title>
        <pubdate>2023-11-24T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Denis Stanescu</author><author>Angela Digulescu</author><author>Cornel Ioana</author><author>Alexandru Serbanescu</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1197590</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1197590</link>
        <title><![CDATA[Detection of OFDM modulations based on the characterization in the phase diagram domain]]></title>
        <pubdate>2023-08-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Angela Digulescu</author><author>Annamaria Sârbu</author><author>Denis Stanescu</author><author>Dragoș Nastasiu</author><author>Cristina Despina-Stoian</author><author>Cornel Ioana</author><author>Ali Mansour</author>
        <description><![CDATA[Signal modulation identification is of high interest for applications in military communications, but is not limited only to this specific field. Some possible applications are related to spectrum surveillance, electronic warfare, quality services, and cognitive radio. Distinguishing between multi-carrier signals, such as orthogonal frequency division multiplexing (OFDM) signals, and single-carrier signals is very important in several applications. Conventional methods face a stalemate in which the classification accuracy process is limited, and, therefore, new descriptors are needed to complement the existing methods. Another drawback is that some features cannot be extracted using conventional feature extraction techniques in practical OFDM systems. This paper introduces a new signal detection algorithm based on the phase diagram characterization. First, the proposed algorithm is described and implemented for simulated signals in MATLAB. Second, the algorithm performance is verified in an experimental scenario by using long-term evolution OFDM signals over a software-defined radio (SDR) frequency testbed. Our findings suggest that the algorithm provides good detection performance in realistic noisy environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1197619</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1197619</link>
        <title><![CDATA[Spread spectrum modulation recognition based on phase diagram entropy]]></title>
        <pubdate>2023-07-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Denis Stanescu</author><author>Angela Digulescu</author><author>Cornel Ioana</author><author>Alexandru Serbanescu</author>
        <description><![CDATA[Wireless communication technologies are undergoing intensive study and are experiencing accelerated progress which leads to a large increase in the number of end-users. Because of this, the radio spectrum has become more crowded than ever. These previously mentioned aspects lead to the urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency. Specifically, modulation scheme recognition occupies a crucial position in the civil and military application, especially with the emergence of Software Defined Radio (SDR). The modulation recognition is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Spread spectrum (SS) techniques represent the foundation for the design of Cognitive Radio systems. In this work, we propose a new method of characterization of Spread spectrum modulations capable of providing relevant information for the process of recognition of this type of modulations. Using the proposed approach, results higher than 90% are obtained in the modulation classification process, thus bringing an advantage over the classical methods, whose performance is below 75%.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.829463</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.829463</link>
        <title><![CDATA[Constant-Beamwidth Kronecker Product Beamforming With Nonuniform Planar Arrays]]></title>
        <pubdate>2022-05-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ariel Frank</author><author>Israel Cohen</author>
        <description><![CDATA[In this paper, we address the problem of constant-beamwidth beamforming using nonuniform planar arrays. We propose two techniques for designing planar beamformers that can maintain different beamwidths in the XZ and YZ planes based on constant-beamwidth linear arrays. In the first technique, we utilize Kronecker product beamforming to find the weights, thus eliminating matrix inversion. The second technique provides a closed-form solution that allows for a tradeoff between white noise gain and directivity factor. The second technique is applicable even when only a subset of the sensors is used. Since our techniques are based on linear arrays, we also consider symmetric linear arrays. We present a method that determines where sensors should be placed to maximize the directivity and increase the frequency range over which the beamwidth remains constant, with a minimal number of sensors. Simulations demonstrate the advantages of the proposed design methods compared to the state-of-the-art. Specifically, our method yields a 1000-fold faster runtime than the competing method, while improving the wideband directivity factor by over 8 dB without compromising the wideband white noise gain in the simulated scenario.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.856968</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.856968</link>
        <title><![CDATA[Att-TasNet: Attending to Encodings in Time-Domain Audio Speech Separation of Noisy, Reverberant Speech Mixtures]]></title>
        <pubdate>2022-05-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>William Ravenscroft</author><author>Stefan Goetze</author><author>Thomas Hain</author>
        <description><![CDATA[Separation of speech mixtures in noisy and reverberant environments remains a challenging task for state-of-the-art speech separation systems. Time-domain audio speech separation networks (TasNets) are among the most commonly used network architectures for this task. TasNet models have demonstrated strong performance on typical speech separation baselines where speech is not contaminated with noise. When additive or convolutive noise is present, performance of speech separation degrades significantly. TasNets are typically constructed of an encoder network, a mask estimation network and a decoder network. The design of these networks puts the majority of the onus for enhancing the signal on the mask estimation network when used without any pre-processing of the input data or post processing of the separation network output data. Use of multihead attention (MHA) is proposed in this work as an additional layer in the encoder and decoder to help the separation network attend to encoded features that are relevant to the target speakers and conversely suppress noisy disturbances in the encoded features. As shown in this work, incorporating MHA mechanisms into the encoder network in particular leads to a consistent performance improvement across numerous quality and intelligibility metrics on a variety of acoustic conditions using the WHAMR corpus, a data-set of noisy reverberant speech mixtures. The use of MHA is also investigated in the decoder network where it is demonstrated that smaller performance improvements are consistently gained within specific model configurations. The best performing MHA models yield a mean 0.6 dB scale invariant signal-to-distortion (SISDR) improvement on noisy reverberant mixtures over a baseline 1D convolution encoder. A mean 1 dB SISDR improvement is observed on clean speech mixtures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.808594</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.808594</link>
        <title><![CDATA[A Deep-Learning Based Framework for Source Separation, Analysis, and Synthesis of Choral Ensembles]]></title>
        <pubdate>2022-04-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pritish Chandna</author><author>Helena Cuesta</author><author>Darius Petermann</author><author>Emilia Gómez</author>
        <description><![CDATA[Choral singing in the soprano, alto, tenor and bass (SATB) format is a widely practiced and studied art form with significant cultural importance. Despite the popularity of the choral setting, it has received little attention in the field of Music Information Retrieval. However, the recent publication of high-quality choral singing datasets as well as recent developments in deep learning based methodologies applied to the field of music and speech processing, have opened new avenues for research in this field. In this paper, we use some of the publicly available choral singing datasets to train and evaluate state-of-the-art source separation algorithms from the speech and music domains for the case of choral singing. Furthermore, we evaluate existing monophonic F0 estimators on the separated unison stems and propose an approximation of the perceived F0 of a unison signal. Additionally, we present a set of applications combining the proposed methodologies, including synthesizing a single singer voice from the unison, and transposing and remixing the separated stems into a synthetic multi-singer choral signal. We finally conduct a set of listening tests to perform a perceptual evaluation of the results we obtain with the proposed methodologies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.794469</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.794469</link>
        <title><![CDATA[Multivariate Lipschitz Analysis of the Stability of Neural Networks]]></title>
        <pubdate>2022-04-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kavya Gupta</author><author>Fateh Kaakai</author><author>Beatrice Pesquet-Popescu</author><author>Jean-Christophe Pesquet</author><author>Fragkiskos D. Malliaros</author>
        <description><![CDATA[The stability of neural networks with respect to adversarial perturbations has been extensively studied. One of the main strategies consist of quantifying the Lipschitz regularity of neural networks. In this paper, we introduce a multivariate Lipschitz constant-based stability analysis of fully connected neural networks allowing us to capture the influence of each input or group of inputs on the neural network stability. Our approach relies on a suitable re-normalization of the input space, with the objective to perform a more precise analysis than the one provided by a global Lipschitz constant. We investigate the mathematical properties of the proposed multivariate Lipschitz analysis and show its usefulness in better understanding the sensitivity of the neural network with regard to groups of inputs. We display the results of this analysis by a new representation designed for machine learning practitioners and safety engineers termed as a Lipschitz star. The Lipschitz star is a graphical and practical tool to analyze the sensitivity of a neural network model during its development, with regard to different combinations of inputs. By leveraging this tool, we show that it is possible to build robust-by-design models using spectral normalization techniques for controlling the stability of a neural network, given a safety Lipschitz target. Thanks to our multivariate Lipschitz analysis, we can also measure the efficiency of adversarial training in inference tasks. We perform experiments on various open access tabular datasets, and also on a real Thales Air Mobility industrial application subject to certification requirements.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.800003</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.800003</link>
        <title><![CDATA[Speech Localization at Low Bitrates in Wireless Acoustics Sensor Networks]]></title>
        <pubdate>2022-03-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mariem Bouafif Mansali</author><author>Pablo Pérez Zarazaga</author><author>Tom Bäckström</author><author>Zied Lachiri</author>
        <description><![CDATA[The use of speech source localization (SSL) and its applications offer great possibilities for the design of speaker local positioning systems with wireless acoustic sensor networks (WASNs). Recent works have shown that data-driven front-ends can outperform traditional algorithms for SSL when trained to work in specific domains, depending on factors like reverberation and noise levels. However, such localization models consider localization directly from raw sensor observations, without consideration for transmission losses in WASNs. In contrast, when sensors reside in separate real-life devices, we need to quantize, encode and transmit sensor data, decreasing the performance of localization, especially when the transmission bitrate is low. In this work, we investigate the effect of low bitrate transmission on a Direction of Arrival (DoA) estimator. We analyze a deep neural network (DNN) based framework performance as a function of the audio encoding bitrate for compressed signals by employing recent communication codecs including PyAWNeS, Opus, EVS, and Lyra. Experimental results show that training the DNN on input encoded with the PyAWNeS codec at 16.4 kB/s can improve the accuracy significantly, and up to 50% of accuracy degradation at a low bitrate for almost all codecs can be recovered. Our results further show that for the best accuracy of the trained model when one of the two channels can be encoded with a bitrate higher than 32 kB/s, it is optimal to have the raw data for the second channel. However, for a lower bitrate, it is preferable to similarly encode the two channels. More importantly, for practical applications, a more generalized model trained with a randomly selected codec for each channel, shows a large accuracy gain when at least one of the two channels is encoded with PyAWNeS.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.819113</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.819113</link>
        <title><![CDATA[Dual-Microphone Speech Reinforcement System With Howling-Control for In-Car Speech Communication]]></title>
        <pubdate>2022-03-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yehav Alkaher</author><author>Israel Cohen </author>
        <description><![CDATA[In this paper, we address the problem of dual-microphone speech reinforcement for improving in-car speech communication via howling control. A speech reinforcement system acquires speech from a speaker’s microphone and delivers it to the other listeners in the car cabin through loudspeakers. A car cabin’s small space makes it vulnerable to acoustic feedback, resulting in the appearance of howling noises. The proposed system aims to maintain a desired high amplification gain over time while not compromising the output speech quality. The dual-microphone system consists of a microphone for speech acquisition and another microphone that monitors the environment for howling detection, where its location depends on its howling detection sensitivity. The proposed algorithm contains a gain-control segment based on the magnitude-slope-deviation measure, which reduces the amplification-gain in the case of howling detection. To find the optimal locations of the howling-detection microphone in the cabin, for a devised set of scenarios, a Pareto optimization method is applied. The Pareto optimization considers the bi-objective nature of the problem, i.e., minimizing both the relative gain-reduction and the overall speech distortion. It is shown that the proposed dual-microphone system outperforms a single-microphone-based system. The performance improvement is demonstrated by showing the higher howling detection sensitivity of the dual-microphone system. Additionally, a microphone constellation design process, for optimal howling detection, is provided through the utilization of the Pareto fronts and anti-fronts approach.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2022.842477</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2022.842477</link>
        <title><![CDATA[AIDA: An Active Inference-Based Design Agent for Audio Processing Algorithms]]></title>
        <pubdate>2022-03-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Albert Podusenko</author><author>Bart van Erp</author><author>Magnus Koudahl</author><author>Bert de Vries</author>
        <description><![CDATA[In this paper we present Active Inference-Based Design Agent (AIDA), which is an active inference-based agent that iteratively designs a personalized audio processing algorithm through situated interactions with a human client. The target application of AIDA is to propose on-the-spot the most interesting alternative values for the tuning parameters of a hearing aid (HA) algorithm, whenever a HA client is not satisfied with their HA performance. AIDA interprets searching for the “most interesting alternative” as an issue of optimal (acoustic) context-aware Bayesian trial design. In computational terms, AIDA is realized as an active inference-based agent with an Expected Free Energy criterion for trial design. This type of architecture is inspired by neuro-economic models on efficient (Bayesian) trial design in brains and implies that AIDA comprises generative probabilistic models for acoustic signals and user responses. We propose a novel generative model for acoustic signals as a sum of time-varying auto-regressive filters and a user response model based on a Gaussian Process Classifier. The full AIDA agent has been implemented in a factor graph for the generative model and all tasks (parameter learning, acoustic context classification, trial design, etc.) are realized by variational message passing on the factor graph. All verification and validation experiments and demonstrations are freely accessible at our GitHub repository.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2021.808395</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2021.808395</link>
        <title><![CDATA[Music Demixing Challenge 2021]]></title>
        <pubdate>2022-01-28T00:00:00Z</pubdate>
        <category>Technology Report</category>
        <author>Yuki Mitsufuji</author><author>Giorgio Fabbro</author><author>Stefan Uhlich</author><author>Fabian-Robert Stöter</author><author>Alexandre Défossez</author><author>Minseok Kim</author><author>Woosung Choi</author><author>Chin-Yun Yu</author><author>Kin-Wai Cheuk</author>
        <description><![CDATA[Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIREX or SiSEC connected state-of-the-art models and corresponding papers, which can help researchers integrate the best practices into their models. In recent years, the widely used MUSDB18 dataset played an important role in measuring the performance of music source separation. While the dataset made a considerable contribution to the advancement of the field, it is also subject to several biases resulting from a focus on Western pop music and a limited number of mixing engineers being involved. To address these issues, we designed the Music Demixing Challenge on a crowd-based machine learning competition platform where the task is to separate stereo songs into four instrument stems (Vocals, Drums, Bass, Other). The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i.e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers. In this paper, we provide the details of the datasets, baselines, evaluation metrics, evaluation results, and technical challenges for future competitions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2021.761559</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2021.761559</link>
        <title><![CDATA[Interference Utilization Precoding in Multi-Cluster IoT Networks]]></title>
        <pubdate>2021-11-22T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Yuanchen Wang</author><author>Eng Gee Lim</author><author>Xiaoping Xue</author><author>Guangyu Zhu</author><author>Rui Pei</author><author>Zhongxiang Wei</author>
        <description><![CDATA[In Internet-of-Things, downlink multi-device interference has long been considered as a harmful element deteriorating system performance, and thus the principle of the classic interference-mitigation based precoding is to suppress the multi-device interference by exploiting the spatial orthogonality. In recent years, a judicious interference utilization precoding has been developed, which is capable of exploiting multi-device interference as a beneficial element for improving device’s reception performance, thus reducing downlink communication latency. In this review paper, we aim to review the emerging interference utilization precoding techniques. We first briefly introduce the concept of constructive interference, and then we present two generic downlink interference-utilization optimizations, which utilizes the multi-device interference for enhancing system performance. Afterwards, the application of interference utilization precoding is discussed in multi-cluster scenario. Finally, some open challenges and future research topics are envisaged.]]></description>
      </item>
      </channel>
    </rss>