Machine Learning in Acoustic Signal Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".
Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 818
Special Issue Editors
Interests: acoustic signal processing; machine learning; array signal processing; source separation
Interests: 3D audio systems and acoustic signal processing; array signal processing and intelligent structures; acoustic and vibration control; nonlinear acoustics; digital signal processing and its applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the last decade, acoustic signal processing techniques have been greatly improved with machine learning. For example, while the conventional algorithms, such as Independent Component Analysis, were based on the statistical characteristics of the target signals, recent algorithms, such as Conv-TasNet, have been developed based on machine learning techniques. In addition, several research areas covered by the conventional acoustic signal processing algorithms, e.g., acoustic echo cancellation or system identification, have also made significant advances with the help of machine learning.
Machine learning not only enhances the acoustic signal processing algorithms but also expands its area of application. In recent research, acoustic signal processing algorithms have been found to be capable of recognizing acoustic events, detecting abnormal sounds, and even compressing acoustic signals or generating desired sound signals, among other notable results.
The Special Issue aims to bring together recent advances in machine learning techniques for the acoustic signal processing problems. The research areas may include (but are not limited to) the following:
- Enhancement or estimation of desired acoustic signals, e.g., noise suppression or source separation;
- Detection or classification of acoustic scene and events;
- Retrieval of music or semantic information from acoustic signals;
- Generative algorithms for acoustic signals;
- Machine learning techniques for compression of the sound signals;
- Machine learning-based underwater acoustic signal processing algorithms.
Dr. Seokjin Lee
Dr. Jun Yang
Guest Editors
Manuscript Submission Information
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Keywords
- acoustic signal processing
- machine learning
- source separation
- sound event detection
- information retrieval
- generative model
- acoustic signal compression
- underwater acoustics
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