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. © 2011 Spr...
With the high increase in the availability of digital music, it has become of interest to automatica...
We introduce a new method for generating time-frequency distributions, which is particularly useful ...
AbstractIn recent years, very large scale online music databases containing more than 10 million tra...
The time-varying frequency structure of musical signals have been analyzed using wavelets by either ...
Musical instrument classification provides a framework for developing and evaluating features for an...
Approach allowing forming wavelet-functions on the basis of periodic signals and signal fragments of...
This paper presents a study on musical signal classification, using wavelet transform analysis in co...
Feature extraction from audio data is a major concern in computer assisted music applications and co...
This project presents a novel method for extracting musical features via signal processing and wavel...
International audienceThere are two major stages in musical genre classification: feature extraction...
A method is described that exhaustively represents the periodicities created by a musical rhythm. Th...
Music information retrieval and particularly musical instrument classification has become a very pop...
In this thesis a novel multiresolution approach for note detection in a polyphonic mix is proposed. ...
We present an automatic method to support melodic pattern discovery by structural analysis of symbol...
Wavelet transform can be applied to many ways such as edge detection, corner detection, filter desig...
With the high increase in the availability of digital music, it has become of interest to automatica...
We introduce a new method for generating time-frequency distributions, which is particularly useful ...
AbstractIn recent years, very large scale online music databases containing more than 10 million tra...
The time-varying frequency structure of musical signals have been analyzed using wavelets by either ...
Musical instrument classification provides a framework for developing and evaluating features for an...
Approach allowing forming wavelet-functions on the basis of periodic signals and signal fragments of...
This paper presents a study on musical signal classification, using wavelet transform analysis in co...
Feature extraction from audio data is a major concern in computer assisted music applications and co...
This project presents a novel method for extracting musical features via signal processing and wavel...
International audienceThere are two major stages in musical genre classification: feature extraction...
A method is described that exhaustively represents the periodicities created by a musical rhythm. Th...
Music information retrieval and particularly musical instrument classification has become a very pop...
In this thesis a novel multiresolution approach for note detection in a polyphonic mix is proposed. ...
We present an automatic method to support melodic pattern discovery by structural analysis of symbol...
Wavelet transform can be applied to many ways such as edge detection, corner detection, filter desig...
With the high increase in the availability of digital music, it has become of interest to automatica...
We introduce a new method for generating time-frequency distributions, which is particularly useful ...
AbstractIn recent years, very large scale online music databases containing more than 10 million tra...