Computational Intelligence for Audio Signal Processing

Dear Colleagues,

In recent years, we have been witnessing an ever-increasing demand for applications of generalized sound recognition technology where the emphasis is placed on non-speech signal processing, i.e., environmental sounds, music, animal vocalizations, etc. The major part of the community makes the assumption that training and future/novel data come from the same distribution. However, in many real-world applications, this assumption may not hold. Hence, it is of paramount importance for the scientific community to develop novel computational methods of audio analysis able to track stationarity changes and adapt the processing mechanism. At the same time, we observed a shift from traditional hand-crafted feature design to a data-driven one which, when combined with deep models, reaches the point of questioning the relevance of traditional audio signal processing. In such solutions, there are still several obstacles to overcome, e.g., a systematic explanation of the operation of learned models, adversarial examples, excessive computational needs, etc. Addressing such obstacles becomes essential, especially in sensitive applications, for examples medical ones, where experts have to make meaningful decisions based on such a recognition algorithm.

We invite original papers, communications, and review articles covering the latest advances in generalized sound recognition technology. Novel solutions for the cases of non-stationary environments and interpretable machine learning algorithms comprise the main priority. Topics include, but are not limited to, the following:

• Computational auditory scene analysis 
• Methodologies, algorithms and techniques for learning in evolving auditory environments
• Sound event detection and recognition
• Audio source separation and localization 
• Audio-based security systems and surveillance 
• Music information retrieval
• Music technology and entertainment
• Computational music composition 
• Interpretable deep learning for audio analysis
• Transfer and reinforcement learning for audio data
• Adversarial machine learning
• Privacy in smart-home assistants
• Audio for mobile and handheld devices 
• Acoustic data processing for the Internet of Things and emerging applications
• Applications to medical audio data
• Biodiversity and environmental monitoring
• Emerging audio technologies (auditory display, interactive sound, and new audio interfaces) 
• Wireless acoustic sensor networks and applications 
• Distributed audio signal processing and coding for segmentation, event detection and alerting.

Prof. Stavros Ntalampiras
Guest Editor

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