Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Sound-source recognition: a theory and computational model
Sound-source recognition: a theory and computational model
MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
Fast Recognition of Musical Genres Using RBF Networks
IEEE Transactions on Knowledge and Data Engineering
Pitch-dependent musical instrument identification and its application to musical sound ontology
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
An Adaptive Method for Subband Decomposition ICA
Neural Computation
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
The Journal of Machine Learning Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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In this paper, we present a system for musical genre classification that uses a preprocessing module to separate corresponding audio signals into three source signals. A feature extraction procedure is applied to each separated signal and the extracted features are fed into an ensemble combination of Support Vector Machine-based classifiers for genre classification. For the source separation task, we examine and compare two relevant algorithms, namely Convolutive Sparse Coding and a Wavelet Packets-based algorithm. We evaluate our system on a music database of four hundred music samples from four different music genres. Experimental results show that there is a higher classification accuracy in applying a source separation algorithm before feature extraction.