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Sparse modeling of chroma features
- Source :
- Signal Processing. 130:105-117
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
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 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 Cramer-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. HighlightsTwo chroma estimators are proposed, exploiting the harmonic structure of music.A sparse modeling framework is used, not requiring explicit model order knowledge.One estimator assumes stationarity, promoting chroma with spectrally smooth partials.One estimator allows for amplitude modulation by using a B-spline representation.A Cramer-Rao lower bound is derived for the chroma-specific signal model.
- Subjects :
- Signal processing
Audio signal
Computer science
Speech recognition
020206 networking & telecommunications
02 engineering and technology
Fundamental frequency
Musical tone
01 natural sciences
Signal
010104 statistics & probability
Tone (musical instrument)
Computer Science::Graphics
Control and Systems Engineering
Signal Processing
Audio analyzer
0202 electrical engineering, electronic engineering, information engineering
Harmonic
Periodogram
Computer Vision and Pattern Recognition
0101 mathematics
Electrical and Electronic Engineering
Algorithm
Software
Subjects
Details
- ISSN :
- 01651684
- Volume :
- 130
- Database :
- OpenAIRE
- Journal :
- Signal Processing
- Accession number :
- edsair.doi...........56a7fb7b80e93d51d500f4a536ff3ab0