Machine Learning in Acoustic Signal Processing

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: 

1. Enhancement or estimation of desired acoustic signals, e.g., noise suppression or source separation;
2. Detection or classification of acoustic scene and events;
3. Retrieval of music or semantic information from acoustic signals;
4. Generative algorithms for acoustic signals;
5. Machine learning techniques for compression of the sound signals;
6. Machine learning-based underwater acoustic signal processing algorithms.

Dr. Seokjin Lee
Dr. Jun Yang
Guest Editors

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