Acoustic signal is one of the hot topics of research in physics and has been studied by many engineers and scientists in various real-world fields, including underwater acoustics, architectural acoustics, engineering acoustics, physical acoustics, environmental acoustics, psychological acoustics, and so on. Noise reduction is the foundation of acoustic signal pre-processing, and the feature extraction for noise reduction signals can obtain useful information from the acoustic signal, which is the linchpin for pattern recognition, target detection, tracking, and localization.
In reality, the research of acoustic signals in various fields is hampered by noise, as real-world signals are usually non-linear and accompanied by intense background noise, and features extracted directly from these signals generally contain a large volume of useless as well as noisy information. Therefore, the study of noise reduction methods of acoustic signals is the first step to effectively utilize this signal, including but not restricted to wavelet analysis, integrated empirical mode decomposition, variational mode decomposition, and consequential improvements. In addition, whether the extracted features contain sufficient usable information also determines the performance of the results of acoustic signal research. For weak signals existing in many fields, it is extremely difficult to precisely describe certain physical meanings of the signal by means of some specific features. In recent years, some researchers have used entropy to characterize the dynamics of the signal, but there are also issues such as missing scale and distance information as well as fractional order differential information. In a word, it is urgent to put forward more advanced features to solve the problem of missing information.
This research topic welcomes research and review articles on advanced acoustic signal noise reduction and feature extraction in various fields. Also, welcome are preeminently appreciated applied contributions to real-world data from any acoustics area. We invite scientists and researchers to contribute original research and critical essays addressing the major issues facing the field, and the potential topics include but are not limited to the following: noise reduction of underwater acoustic signal; entropy-based feature extraction of biomedical signal; variational mode decomposition of bearing fault signal; multi-plane entropy feature extraction; complexity feature extraction of decomposed acoustic signals and applications of artificial intelligence (AI) and machine learning (ML) in feature extraction of acoustic signals.
Acoustic signal is one of the hot topics of research in physics and has been studied by many engineers and scientists in various real-world fields, including underwater acoustics, architectural acoustics, engineering acoustics, physical acoustics, environmental acoustics, psychological acoustics, and so on. Noise reduction is the foundation of acoustic signal pre-processing, and the feature extraction for noise reduction signals can obtain useful information from the acoustic signal, which is the linchpin for pattern recognition, target detection, tracking, and localization.
In reality, the research of acoustic signals in various fields is hampered by noise, as real-world signals are usually non-linear and accompanied by intense background noise, and features extracted directly from these signals generally contain a large volume of useless as well as noisy information. Therefore, the study of noise reduction methods of acoustic signals is the first step to effectively utilize this signal, including but not restricted to wavelet analysis, integrated empirical mode decomposition, variational mode decomposition, and consequential improvements. In addition, whether the extracted features contain sufficient usable information also determines the performance of the results of acoustic signal research. For weak signals existing in many fields, it is extremely difficult to precisely describe certain physical meanings of the signal by means of some specific features. In recent years, some researchers have used entropy to characterize the dynamics of the signal, but there are also issues such as missing scale and distance information as well as fractional order differential information. In a word, it is urgent to put forward more advanced features to solve the problem of missing information.
This research topic welcomes research and review articles on advanced acoustic signal noise reduction and feature extraction in various fields. Also, welcome are preeminently appreciated applied contributions to real-world data from any acoustics area. We invite scientists and researchers to contribute original research and critical essays addressing the major issues facing the field, and the potential topics include but are not limited to the following: noise reduction of underwater acoustic signal; entropy-based feature extraction of biomedical signal; variational mode decomposition of bearing fault signal; multi-plane entropy feature extraction; complexity feature extraction of decomposed acoustic signals and applications of artificial intelligence (AI) and machine learning (ML) in feature extraction of acoustic signals.
