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Score-Informed Source Separation for Music Signals

Authors: Sebastian Ewert and Meinard Müller

Published in: Dagstuhl Follow-Ups, Volume 3, Multimodal Music Processing (2012)

Abstract

In recent years, the processing of audio recordings by exploiting additional musical knowledge has turned out to be a promising research direction. In particular, additional note information as specified by a musical score or a MIDI file has been employed to support various audio processing tasks such as source separation, audio parameterization, performance analysis, or instrument equalization. In this contribution, we provide an overview of approaches for score-informed source separation and illustrate their potential by discussing innovative applications and interfaces. Additionally, to illustrate some basic principles behind these approaches, we demonstrate how score information can be integrated into the well-known non-negative matrix factorization (NMF) framework. Finally, we compare this approach to advanced methods based on parametric models.

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Sebastian Ewert and Meinard Müller. Score-Informed Source Separation for Music Signals. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 73-94, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InCollection{ewert_et_al:DFU.Vol3.11041.73,
  author =	{Ewert, Sebastian and M\"{u}ller, Meinard},
  title =	{{Score-Informed Source Separation for Music Signals}},
  booktitle =	{Multimodal Music Processing},
  pages =	{73--94},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-37-8},
  ISSN =	{1868-8977},
  year =	{2012},
  volume =	{3},
  editor =	{M\"{u}ller, Meinard and Goto, Masataka and Schedl, Markus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.73},
  URN =		{urn:nbn:de:0030-drops-34670},
  doi =		{10.4230/DFU.Vol3.11041.73},
  annote =	{Keywords: Audio processing, music signals, source separation, musical score, alignment, music synchronization, non-negative matrix factorization, parametric mod}
}

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DOI: 10.4230/DagSemProc.09051.3

Case Study ``Beatles Songs'' – What can be Learned from Unreliable Music Alignments?

Authors: Sebastian Ewert, Meinard Müller, Daniel Müllensiefen, Michael Clausen, and Geraint A. Wiggins

Published in: Dagstuhl Seminar Proceedings, Volume 9051, Knowledge representation for intelligent music processing (2009)

Abstract

As a result of massive digitization efforts and the world wide web, there is an exploding amount of available digital data describing and representing music at various semantic levels and in diverse formats. For example, in the case of the Beatles songs, there are numerous recordings including an increasing number of cover songs and arrangements as well as MIDI data and other symbolic music representations. The general goal of music synchronization is to align the multiple information sources related to a given piece of music. This becomes a difficult problem when the various representations reveal significant differences in structure and polyphony, while exhibiting various types of artifacts. In this paper, we address the issue of how music synchronization techniques are useful for automatically revealing critical passages with significant difference between the two versions to be aligned. Using the corpus of the Beatles songs as test bed, we analyze the kind of differences occurring in audio and MIDI versions available for the songs.

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Sebastian Ewert, Meinard Müller, Daniel Müllensiefen, Michael Clausen, and Geraint A. Wiggins. Case Study ``Beatles Songs'' – What can be Learned from Unreliable Music Alignments?. In Knowledge representation for intelligent music processing. Dagstuhl Seminar Proceedings, Volume 9051, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{ewert_et_al:DagSemProc.09051.3,
  author =	{Ewert, Sebastian and M\"{u}ller, Meinard and M\"{u}llensiefen, Daniel and Clausen, Michael and Wiggins, Geraint A.},
  title =	{{Case Study ``Beatles Songs'' – What can be Learned from Unreliable Music Alignments?}},
  booktitle =	{Knowledge representation for intelligent music processing},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9051},
  editor =	{Eleanor Selfridge-Field and Frans Wiering and Geraint A. Wiggins},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09051.3},
  URN =		{urn:nbn:de:0030-drops-19640},
  doi =		{10.4230/DagSemProc.09051.3},
  annote =	{Keywords: MIDI, audio, music synchronization, multimodal, music collections, Beatles songs}
}

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Documents authored by Ewert, Sebastian

Score-Informed Source Separation for Music Signals

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Case Study ``Beatles Songs'' – What can be Learned from Unreliable Music Alignments?

Abstract

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