Methods for multiple pitch tracking and instrument separation from monaural polyphonic recordings

Recently there has been a greater need to analyze, summarize, and categorize the increasing amount of audio content in the world. Most of this content comes from polyphonic music as mixtures of audio sources. Recently there has been much interest in the analysis of polyphonic music. Analysis results can be in the form of source tracking, where instrument pitch tracks and their weights are estimated from a sound mixture throughout time, or they would be in the form of source separation where individual sources are extracted from the mixture. Both problems are addressed in this dissertation. The main problem in the analysis of audio mixtures results from multiple source harmonic frequencies frequently overlapping with each other. Although audio sources are non-stationary, their spectra have a considerable amount of structure that can differentiate them from other sources. Recently non-negative matrix factorization (NMF) and probabilistic latent component analysis (PLCA) have been used by many researchers for the analysis of polyphonic audio. They provide good representations of audio mixtures as sums of individual sources. To solve the multiple instrument tracking problem, a hierarchical probabilistic model is proposed as an extension of probabilistic latent component analysis to include parameter estimation of basis spectra and their relative weights for each instrument and their pitches. A pitch-informed NMF based method is proposed to resolve overlapping harmonics in source separation problems. Both methods were trained in advance on example spectra from similar instruments. Both methods were tested on standard datasets, and they were found to outperform several prior unsupervised state-of-the-art methods addressing similar problems.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Bay, Mert

Contributors dc:contributor

• Beauchamp, James W.
• Downie, J. Stephen
• Hasegawa-Johnson, Mark A.
• Loui, Michael C.
• Smaragdis, Paris

• Signal processing
• Machine learning
• Music information retrieval
• Music signal processing
• Analysis/Synthesis

Statement dc:rights

• Copyright 2012 Mert Bay

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/42375

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/42375