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JA. AR. Barbedo, and A. Lopes, "A Robust and Computationally Efficient Speech/Music Discriminator," J. Audio Eng. Soc., vol. 54, no. 7/8, pp. 571-588, (2006 July.). doi: JA. AR. Barbedo, and A. Lopes, "A Robust and Computationally Efficient Speech/Music Discriminator," J. Audio Eng. Soc., vol. 54 Issue 7/8 pp. 571-588, (2006 July.). doi:
Abstract: A New method for discriminating between speech and music signals is introduced. The strategy is based on the extraction of four features, whose values are combined linearly into a unique parameter. This parameter is used to distinguish between the two kinds of signals. The method has achieved an accuracy superior to 99%, even for severely degraded and noisy signals. Moreover, the low dimensionality of the feature space, together with a very simple information-merging technique, has resulted in a remarkable robustness to new situations. The low computational complexity of the method makes it appropriate for applications that demand real-time operation. Finally excellent resolution for the segmentation of audio streams is achieved by manipulating the analyzed data properly.

@article{barbedo2006a,
author={barbedo, jayme garcia arnal and lopes, amauri},
journal={journal of the audio engineering society},
title={a robust and computationally efficient speech/music discriminator},
year={2006},
volume={54},
number={7/8},
pages={571-588},
doi={},
month={july},} @article{barbedo2006a,
author={barbedo, jayme garcia arnal and lopes, amauri},
journal={journal of the audio engineering society},
title={a robust and computationally efficient speech/music discriminator},
year={2006},
volume={54},
number={7/8},
pages={571-588},
doi={},
month={july},
abstract={a new method for discriminating between speech and music signals is introduced. the strategy is based on the extraction of four features, whose values are combined linearly into a unique parameter. this parameter is used to distinguish between the two kinds of signals. the method has achieved an accuracy superior to 99%, even for severely degraded and noisy signals. moreover, the low dimensionality of the feature space, together with a very simple information-merging technique, has resulted in a remarkable robustness to new situations. the low computational complexity of the method makes it appropriate for applications that demand real-time operation. finally excellent resolution for the segmentation of audio streams is achieved by manipulating the analyzed data properly.},}

TY - paper
TI - A Robust and Computationally Efficient Speech/Music Discriminator
SP - 571 EP - 588
AU - Barbedo, Jayme Garcia Arnal
AU - Lopes, Amauri
PY - 2006
JO - Journal of the Audio Engineering Society
IS - 7/8
VO - 54
VL - 54
Y1 - July 2006 TY - paper
TI - A Robust and Computationally Efficient Speech/Music Discriminator
SP - 571 EP - 588
AU - Barbedo, Jayme Garcia Arnal
AU - Lopes, Amauri
PY - 2006
JO - Journal of the Audio Engineering Society
IS - 7/8
VO - 54
VL - 54
Y1 - July 2006
AB - A New method for discriminating between speech and music signals is introduced. The strategy is based on the extraction of four features, whose values are combined linearly into a unique parameter. This parameter is used to distinguish between the two kinds of signals. The method has achieved an accuracy superior to 99%, even for severely degraded and noisy signals. Moreover, the low dimensionality of the feature space, together with a very simple information-merging technique, has resulted in a remarkable robustness to new situations. The low computational complexity of the method makes it appropriate for applications that demand real-time operation. Finally excellent resolution for the segmentation of audio streams is achieved by manipulating the analyzed data properly.