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.
(Less)
- author
- Kronvall, Ted
LU
; Juhlin, Maria
LU
; Swärd, Johan
LU
; Adalbjörnsson, Stefan I.
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2017-01-01
- 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
- 2025-01-12 13:26:35
@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}},
keywords = {{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}},
doi = {{10.1016/j.sigpro.2016.06.020}},
volume = {{130}},
year = {{2017}},
}