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Sparse Modeling of Chroma Features

Kronvall, Ted LU ; Juhlin, Maria LU ; Swärd, Johan LU ; Adalbjörnsson, Stefan I. LU and Jakobsson, Andreas LU (2017) In Signal Processing 130. p.105-117
Abstract

This work treats the estimation of chroma features for harmonic audio signals using a sparse reconstruction framework. Chroma has been used for decades as a key tool in audio analysis, and is typically formed using a periodogram-based approach that maps the fundamental frequency of a musical tone to its corresponding chroma. Such an approach often leads to problems with tone ambiguity. We address this ambiguity via sparse modeling, allowing us to appropriately penalize ambiguous estimates while taking the harmonic structure of tonal audio into account. Furthermore, we also allow for signals to have time-varying envelopes. Using a spline-based amplitude modulation of the chroma dictionary, the presented estimator is able to model longer... (More)

This work treats the estimation of chroma features for harmonic audio signals using a sparse reconstruction framework. Chroma has been used for decades as a key tool in audio analysis, and is typically formed using a periodogram-based approach that maps the fundamental frequency of a musical tone to its corresponding chroma. Such an approach often leads to problems with tone ambiguity. We address this ambiguity via sparse modeling, allowing us to appropriately penalize ambiguous estimates while taking the harmonic structure of tonal audio into account. Furthermore, we also allow for signals to have time-varying envelopes. Using a spline-based amplitude modulation of the chroma dictionary, the presented estimator is able to model longer frames than what is conventional for audio, as well as to model highly time-localized signals, and signals containing sudden bursts, such as trumpet or trombone signals. Thus, we may retain more signal information as compared to alternative methods. The performances of the proposed methods are evaluated by analyzing the average estimation errors for synthetic signals, as compared to the Cramér–Rao lower bound, and by visual inspection for estimates of real instrument signals. The results show strong visual clarity, as compared to other commonly used methods.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
ADMM, Amplitude modulation, Block sparsity, Chroma, Multi-pitch estimation, Sparse modeling
in
Signal Processing
volume
130
pages
13 pages
publisher
Elsevier
external identifiers
  • scopus:84978043827
  • wos:000386410200011
ISSN
0165-1684
DOI
10.1016/j.sigpro.2016.06.020
language
English
LU publication?
yes
id
3958b4eb-909b-4dcf-8608-245381fe1f5f
date added to LUP
2016-10-19 08:25:48
date last changed
2018-02-18 04:53:02
@article{3958b4eb-909b-4dcf-8608-245381fe1f5f,
  abstract     = {<p>This work treats the estimation of chroma features for harmonic audio signals using a sparse reconstruction framework. Chroma has been used for decades as a key tool in audio analysis, and is typically formed using a periodogram-based approach that maps the fundamental frequency of a musical tone to its corresponding chroma. Such an approach often leads to problems with tone ambiguity. We address this ambiguity via sparse modeling, allowing us to appropriately penalize ambiguous estimates while taking the harmonic structure of tonal audio into account. Furthermore, we also allow for signals to have time-varying envelopes. Using a spline-based amplitude modulation of the chroma dictionary, the presented estimator is able to model longer frames than what is conventional for audio, as well as to model highly time-localized signals, and signals containing sudden bursts, such as trumpet or trombone signals. Thus, we may retain more signal information as compared to alternative methods. The performances of the proposed methods are evaluated by analyzing the average estimation errors for synthetic signals, as compared to the Cramér–Rao lower bound, and by visual inspection for estimates of real instrument signals. The results show strong visual clarity, as compared to other commonly used methods.</p>},
  author       = {Kronvall, Ted and Juhlin, Maria and Swärd, Johan and Adalbjörnsson, Stefan I. and Jakobsson, Andreas},
  issn         = {0165-1684},
  keyword      = {ADMM,Amplitude modulation,Block sparsity,Chroma,Multi-pitch estimation,Sparse modeling},
  language     = {eng},
  month        = {01},
  pages        = {105--117},
  publisher    = {Elsevier},
  series       = {Signal Processing},
  title        = {Sparse Modeling of Chroma Features},
  url          = {http://dx.doi.org/10.1016/j.sigpro.2016.06.020},
  volume       = {130},
  year         = {2017},
}