Characterization of acoustic signals through continuous linear time-frequency representations

Guillemain, Philippe; Kronland‐Martinet, Richard

doi:10.1109/5.488700

Cited by 102 publications

(42 citation statements)

References 8 publications

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“…It is also believed that this knowledge should help in extracting useful information directly from the reassignment vector field, in a statistical signal processing perspective. Finally, it is worthwhile to mention that the obtained results should be of help for providing a statistical basis to related techniques such as the "ridge and skeleton" method [6], whose principle has much to share with reassignment.…”

Section: Resultsmentioning

confidence: 98%

On the Statistics of Spectrogram Reassignment Vectors

Chassande–Mottin¹,

Flandrin²,

Auger³

1998

Recent Developments in Time-Frequency Analysis

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Abstract. Reassignment is a non-linear technique which can improve on the localization of a spectrogram by moving its values according to a suitable vector field. Statistical properties of spectrogram reassignment vectors are investigated in detail. Closed form expressions are given when the observation consists in a non-random component embedded in white Gaussian circular noise, and when the analysis window is Gaussian. An extension to arbitrary windows is also proposed and theoretical claims are supported by numerical simulations.

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Section: Resultsmentioning

confidence: 98%

On the Statistics of Spectrogram Reassignment Vectors

Chassande–Mottin¹,

Flandrin²,

Auger³

1998

Recent Developments in Time-Frequency Analysis

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“…where the amplitude vector function a and the phase vector function solve the di!erential equations (20). The vector function is a constant vector function (equal to ) which depends only on the initial conditions X and X Q .…”

Section: Free Oscillations For the Damped Systemmentioning

confidence: 99%

Identification of Coupled Non-Linear Modes From Free Vibration Using Time-Frequency Representations

Bellizzi

Guillemain

Kronland‐Martinet

2001

Journal of Sound and Vibration

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“…A way of showing the time-frequency information is to define the ridges of the signal. There are several ridge detection methods including stationary phase method which calculates the ridges using stationary point theorem (Delprat et al 1992;Todorovska 2001;Guillemain and Kronland-Martinet 1996), and the simple method which directly finds the local maxima of the scalogram (Mallat 2009;Carmona et al 1997;Todorovska 2001).…”

Section: A Review Of Wavelet Transform and Wavelet Ridgesmentioning

confidence: 99%

Wavelet ridges for musical instrument classification

2011

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The time-varying frequency structure of musical signals have been analyzed using wavelets by either extracting the instantaneous frequency of signals or building features from the energies of sub-band coefficients. We propose to benefit from a combination of these two approaches and use the time-frequency domain energy localization curves, called as wavelet ridges, in order to build features for classification of musical instrument sounds. We evaluated the representative capability of our feature in different musical instrument classification problems using support vector machine classifiers. The comparison with the features based on parameterizing the wavelet sub-band energies confirmed the effectiveness of the proposed feature.

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Characterization of acoustic signals through continuous linear time-frequency representations

Cited by 102 publications

References 8 publications

On the Statistics of Spectrogram Reassignment Vectors

On the Statistics of Spectrogram Reassignment Vectors

Identification of Coupled Non-Linear Modes From Free Vibration Using Time-Frequency Representations

Wavelet ridges for musical instrument classification

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