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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Vydané v:	IEEE transactions on audio, speech, and language processing Ročník 15; číslo 4; s. 1257 - 1272
Hlavní autori:	Thornburg, H., Leistikow, R.J., Berger, J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway, NJ IEEE 01.05.2007 Institute of Electrical and Electronics Engineers
Predmet:	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
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Popis
Shrnutí:	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 pitch attributes while retaining sufficient information for pitch determination and spectral change detection. The system explicitly models note events in terms of transient and steady-state regions as well as possible gaps between note events. In this way, the system readily distinguishes abrupt spectral changes associated with musical onsets from other abrupt change events. Additionally, the method may incorporate melodic context by modeling note-to-note dependences. The method is successfully applied to a variety of piano and violin recordings containing reverberation, effective polyphony due to legato playing style, expressive pitch variations, and background voices. While the method does not provide a sample-accurate segmentation, it facilitates the latter in subsequent processing by isolating musical onsets to frame neighborhoods and identifying possible pitch content before and after the true onset sample location
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	1558-7916
DOI:	10.1109/TASL.2006.889801