Special Issue (Algorithms) 2022 : Machine Understanding of Music and Sound

Special Issue: Machine Understanding of Music and Sound
Special Issue URL: https://www.mdpi.com/journal/algorithms/special_issues/MU_MS

Journal: Algorithms (ISSN 1999-4893)
Journal Publisher: MDPI Open Access Journals
Journal URL: http://www.mdpi.com/journal/algorithms
Instructions for Authors: http://www.mdpi.com/journal/algorithms/instructions

Guest Editor: Patrick Donnelly, Oregon State University
Contact Address: [email protected]

In a world interconnected by accessible data and smart applications, the need for intelligent methods to understand music and sound continues to grow. From computational musicology to music information retrieval, and from emotion recognition to bioacoustic understanding, data-driven algorithms are increasingly employed to analyze, interpret, and generate sound. These diverse applications include the analysis and transformation of sound, detection and classification of audio events, representation and sonification of data, bioacoustic analysis and interpretation, music recommendation and search systems, recognition of emotion or musical genre, automatic transcription and song recognition, and creative endeavors such as composition and sound synthesis.

While new advances in theoretical machine learning continue to be applied to attempt to answer research questions in the domain of non-speech audio, many challenges remain. Some of these challenges include the limited availability of annotated data, complications in the presence of noise, poor generalizability between data sets, difficulties interpreting trained neural network models, and the absence of performance metrics to evaluate creative endeavors. To address these challenges, ongoing research continues to develop new methods and techniques, leverage multimodal information, integrate deep learning and human perception, and apply existing machine learning techniques to novel applications in music and sound.

This Special Issue of the journal Algorithms, entitled "Machine Understanding of Music and Sound", calls for manuscripts proposing novel machine learning and deep learning methods, approaches, and applications that advance a computational understanding of music and sound.

Suggested areas include but are not limited to:
· classification of music/audio
· music recognition
· genre classification
· style recognition
· music similarity
· music recommendation systems
· music sentiment analysis
· music transcription
· score alignment
· expressive performance modeling
· musical style transfer
· music acoustics
· sound synthesis
· algorithmic composition
· intelligent signal processing
· source separation
· evaluation metrics
· bioacoustics

Algorithms (ISSN 1999-4893) is an EI, Scopus, ESCI and MathSciNet indexed Open Access (since 2008) journal, which has received its 2020 CiteScore 2.90, ranking Q2 in the category "Computer Science, Theory & Methods". As open access is supported by the authors and their institutes, an Article Processing Charge (APC) of CHF 1400 currently applies to all accepted papers.