To catch a chorus: using chroma-based representations for audio thumbnailing

Bartsch, Mark A.; Wakefield, Gregory H.

doi:10.1109/aspaa.2001.969531

Cited by 147 publications

(133 citation statements)

References 8 publications

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“…There are other approaches for creating audio thumbnails in the literature. For example, a method was described in [6] which attempted to identify the chorus or refrain of a song by identifying repeated sections of the audio waveform. However, errors may occur in songs that do not meet the structural assumptions upon which the approach was built.…”

Section: Manual Adjustment Of Classification Resultsmentioning

confidence: 99%

<title>Semi-automatic approach for music classification</title>

Zhang

2003

SPIE Proceedings

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Section: Manual Adjustment Of Classification Resultsmentioning

confidence: 99%

<title>Semi-automatic approach for music classification</title>

Zhang

2003

SPIE Proceedings

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“…Foote and Cooper [7] constructed a similarity matrix from midi and audio data, and Wakefield and Bartsch created a similar structure using spectral data. [2,3] This has inspired some of our work. A recent paper by Aucouturier and Sandler takes a different approach to finding pattern in music.…”

Section: Related Workmentioning

confidence: 99%

Discovering Musical Structure in Audio Recordings

Dannenberg

2002

Music and Artificial Intelligence

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Abstract. Music is often described in terms of the structure of repeated phrases. For example, many songs have the form AABA, where each letter represents an instance of a phrase. This research aims to construct descriptions or explanations of music in this form, using only audio recordings as input. A system of programs is described that transcribes the melody of a recording, identifies similar segments, clusters these segments to form patterns, and then constructs an explanation of the music in terms of these patterns. Additional work using spectral information rather than melodic transcription is also described. Examples of successful machine "listening" and music analysis are presented.

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“…Several researchers have developed techniques comparing feature vectors [11,12,13]. Let x i , i ∈ [1, n] be the feature vectors computed for a song.…”

Section: Structural Analysismentioning

confidence: 99%

Mobile music modeling, analysis and recognition

Golik

Harb

Misra

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We present an analysis of music modeling and recognition techniques in the context of mobile music matching, substantially improving on the techniques presented in [1]. We accomplish this by adapting the features specifically to this task, and by introducing new modeling techniques that enable using a corpus of noisy and channel-distorted data to improve mobile music recognition quality. We report the results of an extensive empirical investigation of the system's robustness under realistic channel effects and distortions. We show an improvement of recognition accuracy by explicit duration modeling of music phonemes and by integrating the expected noise environment into the training process. Finally, we propose the use of frame-to-phoneme alignment for high-level structure analysis of polyphonic music.

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To catch a chorus: using chroma-based representations for audio thumbnailing

Cited by 147 publications

References 8 publications

<title>Semi-automatic approach for music classification</title>

<title>Semi-automatic approach for music classification</title>

Discovering Musical Structure in Audio Recordings

Mobile music modeling, analysis and recognition

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