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: 20 May 2025 | Viewed by 737
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- acoustic signal processing
- machine learning
- source separation
- sound event detection
- information retrieval
- generative model
- acoustic signal compression
- underwater acoustics
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue policies can be found here.
