Melody Extraction and Musical Onset Detection via Probabilistic Models of Framewise STFT Peak Data

We propose a probabilistic method for the joint segmentation and melody extraction for musical audio signals which arise from a monophonic score. The method operates on framewise short-time Fourier transform (STFT) peaks, enabling a computationally efficient inference of note onset, duration, and pi...

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Bibliographic Details
Published in:	IEEE transactions on audio, speech, and language processing Vol. 15; no. 4; pp. 1257 - 1272
Main Authors:	Thornburg, H., Leistikow, R.J., Berger, J.
Format:	Journal Article
Language:	English
Published:	Piscataway, NJ IEEE 01.05.2007 Institute of Electrical and Electronics Engineers
Subjects:	Acoustics Applied sciences Audio signals Bayesian methods Cities and towns Context modeling Data mining Detection, estimation, filtering, equalization, prediction Dynamic Bayesian networks Exact sciences and technology Extraction Fourier transforms Inference Information, signal and communications theory Mathematical models Music music transcription onset detection Pianos pitch identification Probabilistic methods Reverberation Robustness Segmentation Signal and communications theory Signal, noise Spectra Steady-state Telecommunications and information theory Fourier transformation Probabilistic approach Segmentation Acoustic signal Change detection Signal estimation Unsteady state Violin pitch identification Modeling Steady state Dynamic Bayesian networks onset detection Audio signal music transcription Bayes network Localization Musical sound Pitch(acoustics) Musical score Musical instrument
ISSN:	1558-7916
Online Access:	Get full text
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