Periodic signal modeling for the octave problem in music transcription

Precise automatic music transcription requires accurate modeling and identification of the spectral content of the audio signal. Whereas a deterministic model in terms of modulated periodic signals allows to distinguish different notes, the presence of multiple notes separated by octaves poses a big...

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Bibliographic Details
Published in2009 16th International Conference on Digital Signal Processing pp. 1 - 6
Main Authors Schutz, A., Slock, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Audio Processing
Cepstrum
Data mining
Frequency estimation
Mobile communication
Multiple signal classification
Music transcription
Periodic signal extraction
Pitch Detection
Signal analysis
Signal detection
Signal processing
Space technology
Telecommunications
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Summary:Precise automatic music transcription requires accurate modeling and identification of the spectral content of the audio signal. Whereas a deterministic model in terms of modulated periodic signals allows to distinguish different notes, the presence of multiple notes separated by octaves poses a big problem since they share the same periodicity, and hence completely overlapping spectral content. In this paper we propose the introduction of a spectral model to allow distinction of such mixtures of spectral content at various octaves. Cyclic correlations are estimated at its pitch and decomposed into even and odd parts, corresponding to even and odd harmonics.
ISBN:9781424432974
1424432979
ISSN:1546-1874
2165-3577
DOI:10.1109/ICDSP.2009.5201127