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