MusicSarreV3, PascalFrancis, Curation, bibRecord, 000009

Automatic Transcription of Recorded Music

Identifieur interne : 000009 ( PascalFrancis/Curation ); précédent : 000008; suivant : 000010

Automatic Transcription of Recorded Music

Auteurs : Peter Grosche [Allemagne] ; Björn Schuller [Allemagne] ; Meinard Müller [Allemagne] ; Gerhard Rigoll [Allemagne]

Source :

Acta acustica united with acustica : (Print) [ 1610-1928 ] ; 2012.

RBID : Pascal:12-0406083

Descripteurs français

Pascal (Inist)
- Acoustique musicale, Enregistrement son, Tonie, Limite spectrale, Sensibilité contexte, Reproduction son, Emission sonore enregistrée, Synchronisation, Transcription automatique, Source sonore, Musique, Annotation, Durée, Transformation Fourier, Modèle Markov caché, Modèle Markov, Résolution temporelle, Analyse multirésolution, Acoustique audio.
Wicri :
- topic : Musique.

English descriptors

KwdEn :
- Annotation, Audio acoustics, Automatic transcription, Context aware, Duration, Fourier transformation, Hidden Markov model, Markov model, Multiresolution analysis, Music, Musical acoustics, Pitch(acoustics), Sound record, Sound recording, Sound reproduction, Sound source, Spectral limit, Synchronization, Time resolution.

Abstract

The automatic transcription of music recordings with the objective to derive a score-like representation from a given audio representation is a fundamental and challenging task. In particular for polyphonic music recordings with overlapping sound sources, current transcription systems still have problems to accurately extract the parameters of individual notes specified by pitch, onset, and duration. In this article, we present a music transcription system that is carefully designed to cope with various facets of music. One main idea of our approach is to consistently employ a mid-level representation that is based on a musically meaningful pitch scale. To achieve the necessary spectral and temporal resolution, we use a multi-resolution Fourier transform enhanced by an instantaneous frequency estimation. Subsequently, having extracted pitch and note onset information from this representation, we employ Hidden Markov Models (HMM) for determining the note events in a context-sensitive fashion. As another contribution, we evaluate our transcription system on an extensive dataset containing audio recordings of various genre. Here, opposed to many previous approaches, we do not only rely on synthetic audio material, but evaluate our system on real audio recordings using MIDI-audio synchronization techniques to automatically generate reference annotations.

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A03		`1`		`@0 Acta aucust. united Acust. : (Print)`
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A08	`01`	`1`	`ENG`	`@1 Automatic Transcription of Recorded Music`
A11	`01`	`1`		`@1 GROSCHE (Peter)`
A11	`02`	`1`		`@1 SCHULLER (Björn)`
A11	`03`	`1`		`@1 MÜLLER (Meinard)`
A11	`04`	`1`		`@1 RIGOLL (Gerhard)`
A14	`01`			`@1 Saarland University and MPI Informatik @2 66123 Saarbrücken @3 DEU @Z 1 aut. @Z 3 aut.`
A14	`02`			`@1 Institute for Human-Machine Communication, Technische Universitat München @2 80333 München @3 DEU @Z 2 aut. @Z 4 aut.`
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C01	`01`		`ENG`	@0 The automatic transcription of music recordings with the objective to derive a score-like representation from a given audio representation is a fundamental and challenging task. In particular for polyphonic music recordings with overlapping sound sources, current transcription systems still have problems to accurately extract the parameters of individual notes specified by pitch, onset, and duration. In this article, we present a music transcription system that is carefully designed to cope with various facets of music. One main idea of our approach is to consistently employ a mid-level representation that is based on a musically meaningful pitch scale. To achieve the necessary spectral and temporal resolution, we use a multi-resolution Fourier transform enhanced by an instantaneous frequency estimation. Subsequently, having extracted pitch and note onset information from this representation, we employ Hidden Markov Models (HMM) for determining the note events in a context-sensitive fashion. As another contribution, we evaluate our transcription system on an extensive dataset containing audio recordings of various genre. Here, opposed to many previous approaches, we do not only rely on synthetic audio material, but evaluate our system on real audio recordings using MIDI-audio synchronization techniques to automatically generate reference annotations.
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C03	`01`	`X`	`SPA`	`@0 Acústica musical @5 06`
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C03	`04`	`X`	`FRE`	`@0 Limite spectrale @5 09`
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C03	`05`	`X`	`FRE`	`@0 Sensibilité contexte @5 10`
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C03	`06`	`X`	`ENG`	`@0 Sound reproduction @5 11`
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C03	`08`	`X`	`FRE`	`@0 Synchronisation @5 16`
C03	`08`	`X`	`ENG`	`@0 Synchronization @5 16`
C03	`08`	`X`	`SPA`	`@0 Sincronización @5 16`
C03	`09`	`X`	`FRE`	`@0 Transcription automatique @5 18`
C03	`09`	`X`	`ENG`	`@0 Automatic transcription @5 18`
C03	`09`	`X`	`SPA`	`@0 Transcripción automática @5 18`
C03	`10`	`X`	`FRE`	`@0 Source sonore @5 19`
C03	`10`	`X`	`ENG`	`@0 Sound source @5 19`
C03	`10`	`X`	`SPA`	`@0 Fuente sonora @5 19`
C03	`11`	`X`	`FRE`	`@0 Musique @5 20`
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C03	`12`	`X`	`ENG`	`@0 Annotation @5 21`
C03	`12`	`X`	`SPA`	`@0 Anotación @5 21`
C03	`13`	`X`	`FRE`	`@0 Durée @5 23`
C03	`13`	`X`	`ENG`	`@0 Duration @5 23`
C03	`13`	`X`	`SPA`	`@0 Duración @5 23`
C03	`14`	`X`	`FRE`	`@0 Transformation Fourier @5 24`
C03	`14`	`X`	`ENG`	`@0 Fourier transformation @5 24`
C03	`14`	`X`	`SPA`	`@0 Transformación Fourier @5 24`
C03	`15`	`X`	`FRE`	`@0 Modèle Markov caché @5 25`
C03	`15`	`X`	`ENG`	`@0 Hidden Markov model @5 25`
C03	`15`	`X`	`SPA`	`@0 Modelo Markov oculto @5 25`
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C03	`16`	`X`	`ENG`	`@0 Markov model @5 26`
C03	`16`	`X`	`SPA`	`@0 Modelo Markov @5 26`
C03	`17`	`X`	`FRE`	`@0 Résolution temporelle @5 33`
C03	`17`	`X`	`ENG`	`@0 Time resolution @5 33`
C03	`17`	`X`	`SPA`	`@0 Resolución temporal @5 33`
C03	`18`	`X`	`FRE`	`@0 Analyse multirésolution @5 34`
C03	`18`	`X`	`ENG`	`@0 Multiresolution analysis @5 34`
C03	`18`	`X`	`SPA`	`@0 Análisis multiresolución @5 34`
C03	`19`	`X`	`FRE`	`@0 Acoustique audio @4 CD @5 96`
C03	`19`	`X`	`ENG`	`@0 Audio acoustics @4 CD @5 96`
C03	`19`	`X`	`SPA`	`@0 Acústica audio @4 CD @5 96`
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Pascal:12-0406083

Le document en format XML

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<s5>16</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Synchronization</s0>
<s5>16</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Sincronización</s0>
<s5>16</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Transcription automatique</s0>
<s5>18</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Automatic transcription</s0>
<s5>18</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Transcripción automática</s0>
<s5>18</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Source sonore</s0>
<s5>19</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Sound source</s0>
<s5>19</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Fuente sonora</s0>
<s5>19</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Musique</s0>
<s5>20</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Music</s0>
<s5>20</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Música</s0>
<s5>20</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Annotation</s0>
<s5>21</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Annotation</s0>
<s5>21</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Anotación</s0>
<s5>21</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Durée</s0>
<s5>23</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Duration</s0>
<s5>23</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Duración</s0>
<s5>23</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Transformation Fourier</s0>
<s5>24</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Fourier transformation</s0>
<s5>24</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Transformación Fourier</s0>
<s5>24</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Modèle Markov caché</s0>
<s5>25</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Hidden Markov model</s0>
<s5>25</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Modelo Markov oculto</s0>
<s5>25</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Modèle Markov</s0>
<s5>26</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Markov model</s0>
<s5>26</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Modelo Markov</s0>
<s5>26</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Résolution temporelle</s0>
<s5>33</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Time resolution</s0>
<s5>33</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Resolución temporal</s0>
<s5>33</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Analyse multirésolution</s0>
<s5>34</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Multiresolution analysis</s0>
<s5>34</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Análisis multiresolución</s0>
<s5>34</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Acoustique audio</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Audio acoustics</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Acústica audio</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fN21><s1>317</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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{{Explor lien
   |wiki=    Wicri/Sarre
   |area=    MusicSarreV3
   |flux=    PascalFrancis
   |étape=   Curation
   |type=    RBID
   |clé=     Pascal:12-0406083
   |texte=   Automatic Transcription of Recorded Music
}}

This area was generated with Dilib version V0.6.33.
Data generation: Sun Jul 15 18:16:09 2018. Site generation: Tue Mar 5 19:21:25 2024

	Serveur d'exploration sur la musique en Sarre
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur la musique en Sarre

Automatic Transcription of Recorded Music

Automatic Transcription of Recorded Music

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

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