Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening

Dear Colleagues,

Recent introduction of Deep Learning has led to a vast array of breakthroughs in many fields of science and engineering. The data-driven approach has gathered the attention of research communities and has often been successful in yielding solutions to very complex classification and regression problems.

In the fields of audio analysis, processing and acoustic modelling, Deep Learning has been adopted, initially borrowing their methods from the image processing and computer vision field, and then finding creative and innovative solutions to suit domain-specific needs of acoustic research. In this process, researchers are facing two big challenges: learning meaningful spatio-temporal representations of audio signals and making sense of the black-box model of neural networks, i.e. extracting knowledge that is useful for scientific advance.

In this special issue, we welcome the submission of papers dealing with novel computational methods involving modelling, parametrization, and knowledge extraction of acoustic data. The considered topics include, e.g.:

• Applications of Deep Learning to sound synthesis
• Control and estimation problems in physical modeling
• Intelligent music production and novel digital audio effects
• Representation learning and/or transfer of musical composition and performance characteristics including, timbre, style and playing technique
• Analysis and modelling of acoustic phenomena including musical acoustics, speech signals, room acoustics, environmental, ecological, medical and machine sounds.
• Machine listening and perception models inspired by human hearing
• Application of Deep Learning to wave propagation problems in fluids and solids

We aim at fostering good research practices in Deep Learning. Considering current scientific and ethical concerns with Deep Learning, including reproducibility and explainability, we strongly support works that are based on open datasets and source code, works that excel on the scientific method, and works providing evidences and explanations for the observed phenomena.

Dr. Leonardo Gabrielli
Dr. George Fazekas
Dr. Juhan Nam
Guest Editors

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