Apologies for cross-posting.
Dear all,
Please find the call for a special issue on " Machine Learning Applied to
Music/Audio Signal Processing" in MDPI Electronics at
https://www.mdpi.com/si/51394
Thanks and we are looking forward to your contributions!
---
Dear Colleagues,
The applications of audio and music processing range from music discovery and
recommendation systems over speech enhancement, audio event detection, and
music transcription, to creative applications such as sound synthesis and
morphing.
The last decade has seen a paradigm shift from expert-designed algorithms to
data-driven approaches. Machine learning approaches, and Deep Neural Networks
specifically, have been shown to outperform traditional approaches on a large
variety of tasks including audio classification, source separation,
enhancement, and content analysis. With data-driven approaches, however, came a
set of new challenges. Two of these challenges are training data and
interpretability. As supervised machine learning approaches increase in
complexity, the increasing need for more annotated training data can often not
be matched with available data. The lack of understanding of how data are
modeled by neural networks can lead to unexpected results and open
vulnerabilities for adversarial attacks.
The main aim of this Special Issue is to seek high-quality submissions that
present novel data-driven methods for audio/music signal processing and
analysis and address main challenges of applying machine learning to audio
signals. Within the general area of audio and music information retrieval as
well as audio and music processing, the topics of interest include, but are not
limited to, the following:
- unsupervised and semi-supervised systems for audio/music processing and
analysis
- machine learning methods for raw audio signal analysis and transformation
- approaches to understanding and controlling the behavior of audio
processing systems such as visualization, auralization, or regularization
methods
- generative systems for sound synthesis and transformation
- adversarial attacks and the identification of 'deepfakes' in audio and
music
- audio and music style transfer methods
- audio recording and music production parameter estimation
- data collection methods, active learning, and interactive machine
learning for data-driven approaches
Dr. Peter Knees
Dr. Alexander Lerch
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