Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Adaptive signal processing for interference cancellation in hearing aids
Signal Processing
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Sound classification in hearing aids inspired by auditory scene analysis
EURASIP Journal on Applied Signal Processing
Adaptive feedback cancellation for audio applications
Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
Neural networks for speech separation for binaural hearing aids
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
Application of neural networks to speech/music/noise classification in digital hearing aids
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
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In this paper we propose a method to generate a novel set of features in order to improve sound classification in digital hearing aids. The approach is based on the fact that those classification algorithms whose design consists in minimizing the mean squared error work better when the data to be classified exhibit a Gaussian distribution. The novel features we propose are thus based on sound spectral magnitudes that, prior to the feature calculation itself, are Gaussianized by a power law parametrized by a design parameter, @a. The explored method allows to jointly design the sound features and a least-square linear classifier, whose design parameters are also parametrized by @a. The experimental work suggests that there is a proper value of @a for which the so-designed classifier, fed with the novel features, exhibits a low error probability. Moreover, we have found that the method can be extended to nonlinear classifiers also trained by minimizing the mean squared error, such as, for instance, neural networks.