22 Search Results for "M�ller, Meinard"


Document
Deep Learning and Knowledge Integration for Music Audio Analysis (Dagstuhl Seminar 22082)

Authors: Meinard Müller, Rachel Bittner, Juhan Nam, Michael Krause, and Yigitcan Özer

Published in: Dagstuhl Reports, Volume 12, Issue 2 (2022)


Abstract
Given the increasing amount of digital music, the development of computational tools that allow users to find, organize, analyze, and interact with music has become central to the research field known as Music Information Retrieval (MIR). As in general multimedia processing, many of the recent advances in MIR have been driven by techniques based on deep learning (DL). There is a growing trend to relax problem-specific modeling constraints from MIR systems and instead apply relatively generic DL-based approaches that rely on large quantities of data. In the Dagstuhl Seminar 22082, we critically examined this trend, discussing the strengths and weaknesses of these approaches using music as a challenging application domain. We mainly focused on music analysis tasks applied to audio representations (rather than symbolic music representations) to give the seminar cohesion. In this context, we systematically explored how musical knowledge can be integrated into or relaxed from computational pipelines. We then discussed how this choice could affect the explainability of models or the vulnerability to data biases and confounding factors. Furthermore, besides explainability and generalization, we also addressed efficiency, ethical and educational aspects considering traditional model-based and recent data-driven methods. In this report, we give an overview of the various contributions and results of the seminar. We start with an executive summary describing the main topics, goals, and group activities. Then, we give an overview of the participants' stimulus talks and subsequent discussions (listed alphabetically by the main contributor’s last name) and summarize further activities, including group discussions and music sessions.

Cite as

Meinard Müller, Rachel Bittner, Juhan Nam, Michael Krause, and Yigitcan Özer. Deep Learning and Knowledge Integration for Music Audio Analysis (Dagstuhl Seminar 22082). In Dagstuhl Reports, Volume 12, Issue 2, pp. 103-133, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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@Article{muller_et_al:DagRep.12.2.103,
  author =	{M\"{u}ller, Meinard and Bittner, Rachel and Nam, Juhan and Krause, Michael and \"{O}zer, Yigitcan},
  title =	{{Deep Learning and Knowledge Integration for Music Audio Analysis (Dagstuhl Seminar 22082)}},
  pages =	{103--133},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{2},
  editor =	{M\"{u}ller, Meinard and Bittner, Rachel and Nam, Juhan and Krause, Michael and \"{O}zer, Yigitcan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.2.103},
  URN =		{urn:nbn:de:0030-drops-169333},
  doi =		{10.4230/DagRep.12.2.103},
  annote =	{Keywords: Audio signal processing, deep learning, knowledge representation, music information retrieval, user interaction and interfaces}
}
Document
Computational Methods for Melody and Voice Processing in Music Recordings (Dagstuhl Seminar 19052)

Authors: Meinard Müller, Emilia Gómez, and Yi-Hsun Yang

Published in: Dagstuhl Reports, Volume 9, Issue 1 (2019)


Abstract
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.

Cite as

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)


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@Article{muller_et_al:DagRep.9.1.125,
  author =	{M\"{u}ller, Meinard and G\'{o}mez, Emilia and Yang, Yi-Hsun},
  title =	{{Computational Methods for Melody and Voice Processing in Music Recordings (Dagstuhl Seminar 19052)}},
  pages =	{125--177},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{9},
  number =	{1},
  editor =	{M\"{u}ller, Meinard and G\'{o}mez, Emilia and Yang, Yi-Hsun},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.9.1.125},
  URN =		{urn:nbn:de:0030-drops-105732},
  doi =		{10.4230/DagRep.9.1.125},
  annote =	{Keywords: 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}
}
Document
Computational Music Structure Analysis (Dagstuhl Seminar 16092)

Authors: Meinard Müller, Elaine Chew, and Juan Pablo Bello

Published in: Dagstuhl Reports, Volume 6, Issue 2 (2016)


Abstract
Music is a ubiquitous and vital part of the lives of billions of people worldwide. Musical creations and performances are among the most complex and intricate of our cultural artifacts, and the emotional power of music can touch us in surprising and profound ways. In view of the rapid and sustained growth of digital music sharing and distribution, the development of computational methods to help users find and organize music information has become an important field of research in both industry and academia. The Dagstuhl Seminar 16092 was devoted to a research area known as music structure analysis, where the general objective is to uncover patterns and relationships that govern the organization of notes, events, and sounds in music. Gathering researchers from different fields, we critically reviewed the state of the art for computational approaches to music structure analysis in order to identify the main limitations of existing methodologies. This triggered interdisciplinary discussions that leveraged insights from fields as disparate as psychology, music theory, composition, signal processing, machine learning, and information sciences to address the specific challenges of understanding structural information in music. Finally, 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 list of abstracts giving a more detailed overview of the participants' contributions as well as of the ideas and results discussed in the group meetings of our seminar.

Cite as

Meinard Müller, Elaine Chew, and Juan Pablo Bello. Computational Music Structure Analysis (Dagstuhl Seminar 16092). In Dagstuhl Reports, Volume 6, Issue 2, pp. 147-190, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)


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@Article{muller_et_al:DagRep.6.2.147,
  author =	{M\"{u}ller, Meinard and Chew, Elaine and Bello, Juan Pablo},
  title =	{{Computational Music Structure Analysis (Dagstuhl Seminar 16092)}},
  pages =	{147--190},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{2},
  editor =	{M\"{u}ller, Meinard and Chew, Elaine and Bello, Juan Pablo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.6.2.147},
  URN =		{urn:nbn:de:0030-drops-61415},
  doi =		{10.4230/DagRep.6.2.147},
  annote =	{Keywords: Music Information Retrieval, Music Processing, Music Perception and Cognition, Music Composition and Performance, Knowledge Representation, User Interaction and Interfaces, Audio Signal Processing, Machine Learning}
}
Document
Computational Audio Analysis (Dagstuhl Seminar 13451)

Authors: Meinard Müller, Shrikanth S. Narayanan, and Björn Schuller

Published in: Dagstuhl Reports, Volume 3, Issue 11 (2014)


Abstract
Compared to traditional speech, music, or sound processing, the computational analysis of general audio data has a relatively young research history. In particular, the extraction of affective information (i.e., information that does not deal with the 'immediate' nature of the content such as the spoken words or note events) from audio signals has become an important research strand with a huge increase of interest in academia and industry. At an early stage of this novel research direction, many analysis techniques and representations were simply transferred from the speech domain to other audio domains. However, general audio signals (including their affective aspects) typically possess acoustic and structural characteristics that distinguish them from spoken language or isolated `controlled' music or sound events. In the Dagstuhl Seminar 13451 titled "Computational Audio Analysis" we discussed the development of novel machine learning as well as signal processing techniques that are applicable for a wide range of audio signals and analysis tasks. In particular, we looked at a variety of sounds besides speech such as music recordings, animal sounds, environmental sounds, and mixtures thereof. 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, one finds a list of abstracts giving a more detailed overview of the participants' contributions as well as of the ideas and results discussed in the group meetings of our seminar. To conclude, an attempt is made to define the field as given by the views of the participants.

Cite as

Meinard Müller, Shrikanth S. Narayanan, and Björn Schuller. Computational Audio Analysis (Dagstuhl Seminar 13451). In Dagstuhl Reports, Volume 3, Issue 11, pp. 1-28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)


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@Article{muller_et_al:DagRep.3.11.1,
  author =	{M\"{u}ller, Meinard and Narayanan, Shrikanth S. and Schuller, Bj\"{o}rn},
  title =	{{Computational Audio Analysis (Dagstuhl Seminar 13451)}},
  pages =	{1--28},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{3},
  number =	{11},
  editor =	{M\"{u}ller, Meinard and Narayanan, Shrikanth S. and Schuller, Bj\"{o}rn},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.3.11.1},
  URN =		{urn:nbn:de:0030-drops-44346},
  doi =		{10.4230/DagRep.3.11.1},
  annote =	{Keywords: Audio Analysis, Signal Processing, Machine Learning, Sound, Speech, Music, Affective Computing}
}
Document
Complete Volume
DFU, Volume 3, Multimodal Music Processing, Complete Volume

Authors: Meinard Müller, Masataka Goto, and Markus Schedl

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


Abstract
DFU, Volume 3, Multimodal Music Processing, Complete Volume

Cite as

Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@Collection{DFU.Vol3.11041,
  title =	{{DFU, Volume 3, Multimodal Music Processing, Complete Volume}},
  booktitle =	{Multimodal Music Processing},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041},
  URN =		{urn:nbn:de:0030-drops-36023},
  doi =		{10.4230/DFU.Vol3.11041},
  annote =	{Keywords: Sound and Music Computing, Arts and Humanities–Music, Multimedia Information Systems}
}
Document
Frontmatter, Table of Contents, Preface, List of Authors

Authors: Meinard Müller, Masataka Goto, and Markus Schedl

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


Abstract
Frontmatter, Table of Contents, Preface, List of Authors

Cite as

Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{muller_et_al:DFU.Vol3.11041.i,
  author =	{M\"{u}ller, Meinard and Goto, Masataka and Schedl, Markus},
  title =	{{Frontmatter, Table of Contents, Preface, List of Authors}},
  booktitle =	{Multimodal Music Processing},
  pages =	{0:i--0:xii},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.i},
  URN =		{urn:nbn:de:0030-drops-34621},
  doi =		{10.4230/DFU.Vol3.11041.i},
  annote =	{Keywords: Frontmatter, Table of Contents, Preface, List of Authors}
}
Document
Linking Sheet Music and Audio - Challenges and New Approaches

Authors: Verena Thomas, Christian Fremerey, Meinard Müller, and Michael Clausen

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


Abstract
Score and audio files are the two most important ways to represent, convey, record, store, and experience music. While score describes a piece of music on an abstract level using symbols such as notes, keys, and measures, audio files allow for reproducing a specific acoustic realization of the piece. Each of these representations reflects different facets of music yielding insights into aspects ranging from structural elements (e.g., motives, themes, musical form) to specific performance aspects (e.g., artistic shaping, sound). Therefore, the simultaneous access to score and audio representations is of great importance. In this paper, we address the problem of automatically generating musically relevant linking structures between the various data sources that are available for a given piece of music. In particular, we discuss the task of sheet music-audio synchronization with the aim to link regions in images of scanned scores to musically corresponding sections in an audio recording of the same piece. Such linking structures form the basis for novel interfaces that allow users to access and explore multimodal sources of music within a single framework. As our main contributions, we give an overview of the state-of-the-art for this kind of synchronization task, we present some novel approaches, and indicate future research directions. In particular, we address problems that arise in the presence of structural differences and discuss challenges when applying optical music recognition to complex orchestral scores. Finally, potential applications of the synchronization results are presented.

Cite as

Verena Thomas, Christian Fremerey, Meinard Müller, and Michael Clausen. Linking Sheet Music and Audio - Challenges and New Approaches. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{thomas_et_al:DFU.Vol3.11041.1,
  author =	{Thomas, Verena and Fremerey, Christian and M\"{u}ller, Meinard and Clausen, Michael},
  title =	{{Linking Sheet Music and Audio - Challenges and New Approaches}},
  booktitle =	{Multimodal Music Processing},
  pages =	{1--22},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.1},
  URN =		{urn:nbn:de:0030-drops-34637},
  doi =		{10.4230/DFU.Vol3.11041.1},
  annote =	{Keywords: Music signals, audio, sheet music, music synchronization, alignment, optical music recognition, user interfaces, multimodality}
}
Document
Lyrics-to-Audio Alignment and its Application

Authors: Hiromasa Fujihara and Masataka Goto

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


Abstract
Automatic lyrics-to-audio alignment techniques have been drawing attention in the last years and various studies have been made in this field. The objective of lyrics-to-audio alignment is to estimate a temporal relationship between lyrics and musical audio signals and can be applied to various applications such as Karaoke-style lyrics display. In this contribution, we provide an overview of recent development in this research topic, where we put a particular focus on categorization of various methods and on applications.

Cite as

Hiromasa Fujihara and Masataka Goto. Lyrics-to-Audio Alignment and its Application. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 23-36, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{fujihara_et_al:DFU.Vol3.11041.23,
  author =	{Fujihara, Hiromasa and Goto, Masataka},
  title =	{{Lyrics-to-Audio Alignment and its Application}},
  booktitle =	{Multimodal Music Processing},
  pages =	{23--36},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.23},
  URN =		{urn:nbn:de:0030-drops-34644},
  doi =		{10.4230/DFU.Vol3.11041.23},
  annote =	{Keywords: Lyrics, Alignment, Karaoke, Multifunctional music player, Lyrics-based music retrieval}
}
Document
Fusion of Multimodal Information in Music Content Analysis

Authors: Slim Essid and Gaël Richard

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


Abstract
Music is often processed through its acoustic realization. This is restrictive in the sense that music is clearly a highly multimodal concept where various types of heterogeneous information can be associated to a given piece of music (a musical score, musicians' gestures, lyrics, user-generated metadata, etc.). This has recently led researchers to apprehend music through its various facets, giving rise to "multimodal music analysis" studies. This article gives a synthetic overview of methods that have been successfully employed in multimodal signal analysis. In particular, their use in music content processing is discussed in more details through five case studies that highlight different multimodal integration techniques. The case studies include an example of cross-modal correlation for music video analysis, an audiovisual drum transcription system, a description of the concept of informed source separation, a discussion of multimodal dance-scene analysis, and an example of user-interactive music analysis. In the light of these case studies, some perspectives of multimodality in music processing are finally suggested.

Cite as

Slim Essid and Gaël Richard. Fusion of Multimodal Information in Music Content Analysis. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 37-52, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{essid_et_al:DFU.Vol3.11041.37,
  author =	{Essid, Slim and Richard, Ga\"{e}l},
  title =	{{Fusion of Multimodal Information in Music Content Analysis}},
  booktitle =	{Multimodal Music Processing},
  pages =	{37--52},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.37},
  URN =		{urn:nbn:de:0030-drops-34652},
  doi =		{10.4230/DFU.Vol3.11041.37},
  annote =	{Keywords: Multimodal music processing, music signals indexing and transcription, information fusion, audio, video}
}
Document
A Cross-Version Approach for Harmonic Analysis of Music Recordings

Authors: Verena Konz and Meinard Müller

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


Abstract
The automated extraction of chord labels from audio recordings is a central task in music information retrieval. Here, the chord labeling is typically performed on a specific audio version of a piece of music, produced under certain recording conditions, played on specific instruments and characterized by individual styles of the musicians. As a consequence, the obtained chord labeling results are strongly influenced by version-dependent characteristics. In this chapter, we show that analyzing the harmonic properties of several audio versions synchronously stabilizes the chord labeling result in the sense that inconsistencies indicate version-dependent characteristics, whereas consistencies across several versions indicate harmonically stable passages in the piece of music. In particular, we show that consistently labeled passages often correspond to correctly labeled passages. Our experiments show that the cross-version labeling procedure significantly increases the precision of the result while keeping the recall at a relatively high level. Furthermore, we introduce a powerful visualization which reveals the harmonically stable passages on a musical time axis specified in bars. Finally, we demonstrate how this visualization facilitates a better understanding of classification errors and may be used by music experts as a helpful tool for exploring harmonic structures.

Cite as

Verena Konz and Meinard Müller. A Cross-Version Approach for Harmonic Analysis of Music Recordings. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 53-72, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{konz_et_al:DFU.Vol3.11041.53,
  author =	{Konz, Verena and M\"{u}ller, Meinard},
  title =	{{A Cross-Version Approach for Harmonic Analysis of Music Recordings}},
  booktitle =	{Multimodal Music Processing},
  pages =	{53--72},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.53},
  URN =		{urn:nbn:de:0030-drops-34665},
  doi =		{10.4230/DFU.Vol3.11041.53},
  annote =	{Keywords: Harmonic analysis, chord labeling, audio, music, music synchronization, audio alignment}
}
Document
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.

Cite as

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-dev.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}
}
Document
Music Information Retrieval Meets Music Education

Authors: Christian Dittmar, Estefanía Cano, Jakob Abeßer, and Sascha Grollmisch

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


Abstract
This paper addresses the use of Music Information Retrieval (MIR) techniques in music education and their integration in learning software. A general overview of systems that are either commercially available or in research stage is presented. Furthermore, three well-known MIR methods used in music learning systems and their state-of-the-art are described: music transcription, solo and accompaniment track creation, and generation of performance instructions. As a representative example of a music learning system developed within the MIR community, the Songs2See software is outlined. Finally, challenges and directions for future research are described.

Cite as

Christian Dittmar, Estefanía Cano, Jakob Abeßer, and Sascha Grollmisch. Music Information Retrieval Meets Music Education. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 95-120, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{dittmar_et_al:DFU.Vol3.11041.95,
  author =	{Dittmar, Christian and Cano, Estefan{\'\i}a and Abe{\ss}er, Jakob and Grollmisch, Sascha},
  title =	{{Music Information Retrieval Meets Music Education}},
  booktitle =	{Multimodal Music Processing},
  pages =	{95--120},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.95},
  URN =		{urn:nbn:de:0030-drops-34689},
  doi =		{10.4230/DFU.Vol3.11041.95},
  annote =	{Keywords: Music learning, music transcription, source separation, performance feedback}
}
Document
Human Computer Music Performance

Authors: Roger B. Dannenberg

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


Abstract
Human Computer Music Performance (HCMP) is the study of music performance by live human performers and real-time computer-based performers. One goal of HCMP is to create a highly autonomous artificial performer that can fill the role of a human, especially in a popular music setting. This will require advances in automated music listening and understanding, new representations for music, techniques for music synchronization, real-time human-computer communication, music generation, sound synthesis, and sound diffusion. Thus, HCMP is an ideal framework to motivate and integrate advanced music research. In addition, HCMP has the potential to benefit millions of practicing musicians, both amateurs and professionals alike. The vision of HCMP, the problems that must be solved, and some recent progress are presented.

Cite as

Roger B. Dannenberg. Human Computer Music Performance. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 121-134, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{dannenberg:DFU.Vol3.11041.121,
  author =	{Dannenberg, Roger B.},
  title =	{{Human Computer Music Performance}},
  booktitle =	{Multimodal Music Processing},
  pages =	{121--134},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.121},
  URN =		{urn:nbn:de:0030-drops-34693},
  doi =		{10.4230/DFU.Vol3.11041.121},
  annote =	{Keywords: Interactive performance, music processing, music signals, music analysis, music synthesis, audio, score}
}
Document
User-Aware Music Retrieval

Authors: Markus Schedl, Sebastian Stober, Emilia Gómez, Nicola Orio, and Cynthia C.S. Liem

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


Abstract
Personalized and user-aware systems for retrieving multimedia items are becoming increasingly important as the amount of available multimedia data has been spiraling. A personalized system is one that incorporates information about the user into its data processing part (e.g., a particular user taste for a movie genre). A context-aware system, in contrast, takes into account dynamic aspects of the user context when processing the data (e.g., location and time where/when a user issues a query). Today's user-adaptive systems often incorporate both aspects. Particularly focusing on the music domain, this article gives an overview of different aspects we deem important to build personalized music retrieval systems. In this vein, we first give an overview of factors that influence the human perception of music. We then propose and discuss various requirements for a personalized, user-aware music retrieval system. Eventually, the state-of-the-art in building such systems is reviewed, taking in particular aspects of "similarity" and "serendipity" into account.

Cite as

Markus Schedl, Sebastian Stober, Emilia Gómez, Nicola Orio, and Cynthia C.S. Liem. User-Aware Music Retrieval. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 135-156, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{schedl_et_al:DFU.Vol3.11041.135,
  author =	{Schedl, Markus and Stober, Sebastian and G\'{o}mez, Emilia and Orio, Nicola and Liem, Cynthia C.S.},
  title =	{{User-Aware Music Retrieval}},
  booktitle =	{Multimodal Music Processing},
  pages =	{135--156},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.135},
  URN =		{urn:nbn:de:0030-drops-34709},
  doi =		{10.4230/DFU.Vol3.11041.135},
  annote =	{Keywords: user-aware music retrieval, personalization, recommendation, user context, adaptive systems, similarity measurement, serendipity}
}
Document
Audio Content-Based Music Retrieval

Authors: Peter Grosche, Meinard Müller, and Joan Serrà

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


Abstract
The rapidly growing corpus of digital audio material requires novel retrieval strategies for exploring large music collections. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. In this contribution, we discuss content-based retrieval strategies that follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. Such strategies can be loosely classified according to their "specificity", which refers to the degree of similarity between the query and the database documents. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Furthermore, we introduce a second classification principle based on "granularity", where one distinguishes between fragment-level and document-level retrieval. Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise "audio identification", "audio matching", and "version identification". For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework.

Cite as

Peter Grosche, Meinard Müller, and Joan Serrà. Audio Content-Based Music Retrieval. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 157-174, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


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@InCollection{grosche_et_al:DFU.Vol3.11041.157,
  author =	{Grosche, Peter and M\"{u}ller, Meinard and Serr\`{a}, Joan},
  title =	{{Audio Content-Based Music Retrieval}},
  booktitle =	{Multimodal Music Processing},
  pages =	{157--174},
  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-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.157},
  URN =		{urn:nbn:de:0030-drops-34711},
  doi =		{10.4230/DFU.Vol3.11041.157},
  annote =	{Keywords: music retrieval, content-based, query-by-example, audio identification, audio matching, cover song identification}
}
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