Computational Methods for Melody and Voice Processing in Music Recordings (Dagstuhl Seminar 19052)

Authors Meinard Müller, Emilia Gómez, Yi-Hsun Yang and all authors of the abstracts in this report

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Meinard Müller
Emilia Gómez
Yi-Hsun Yang
and all authors of the abstracts in this report

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Meinard Müller, Emilia Gómez, and Yi-Hsun Yang. Computational Methods for Melody and Voice Processing in Music Recordings (Dagstuhl Seminar 19052). In Dagstuhl Reports, Volume 9, Issue 1, pp. 125-177, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


In our daily lives, we are constantly surrounded by music, and we are deeply influenced by music. Making music together can create strong ties between people, while fostering communication and creativity. This is demonstrated, for example, by the large community of singers active in choirs or by the fact that music constitutes an important part of our cultural heritage. The availability of music in digital formats and its distribution over the world wide web has changed the way we consume, create, enjoy, explore, and interact with music. To cope with the increasing amount of digital music, one requires computational methods and tools that allow users to find, organize, analyze, and interact with music--topics that are central to the research field known as \emph{Music Information Retrieval} (MIR). The Dagstuhl Seminar 19052 was devoted to a branch of MIR that is of particular importance: processing melodic voices (with a focus on singing voices) using computational methods. It is often the melody, a specific succession of musical tones, which constitutes the leading element in a piece of music. In the seminar we discussed how to detect, extract, and analyze melodic voices as they occur in recorded performances of a piece of music. Gathering researchers from different fields, we critically reviewed the state of the art of computational approaches to various MIR tasks related to melody processing including pitch estimation, source separation, singing voice analysis and synthesis, and performance analysis (timbre, intonation, expression). This triggered interdisciplinary discussions that leveraged insights from fields as disparate as audio processing, machine learning, music perception, music theory, and information retrieval. In particular, we discussed current challenges in academic and industrial research in view of the recent advances in deep learning and data-driven models. Furthermore, we explored novel applications of these technologies in music and multimedia retrieval, content creation, musicology, education, and human-computer interaction. In this report, we give an overview of the various contributions and results of the seminar. We start with an executive summary, which describes the main topics, goals, and group activities. Then, we present a more detailed overview of the participants' contributions (listed alphabetically by their last names) as well as of the ideas, results, and activities of the group meetings, the demo, and the music sessions.
  • Acoustics of singing
  • audio signal processing
  • machine learning
  • music composition and performance
  • music information retrieval
  • music perception and cognition
  • music processing
  • singing voice processing
  • sound source separation
  • user interaction and interfaces


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