Special Issue "Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening"
Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 34418
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
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 2300 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.
- Deep learning
- Sound synthesis
- Machine listening
- Audio signal processing
- Sound event detection
- Acoustic modelling
- Digital audio effects
- Audio style transfer