Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Recognition and analysis of audio for copyright protection: the RAA project
Journal of the American Society for Information Science and Technology - Music information retrieval
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Real-time discrimination of broadcast speech/music
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
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This paper focuses on the development of an automatic sound classifier for digital hearing aids that aims to enhance the listening comprehension when the user goes from a sound environment to another different one. The approach consists in dividing the classifying algorithm into two layers that make use of two-class algorithms that work more efficiently: the input signal discriminated by the first layer into either speech or non-speech is ulteriorly classified more specifically depending on whether the user is in a conversation (both in quiet or in the presence of background noise) or in a noisy ambient in the absent of speech. The system results in having four classes, labeled speech in quiet, speech in noise, stationary noisy environments (for instance, an aircraft cabin), and non-stationary noisy environments. The combination of classifiers that has been found to be more successful in terms of probability of correct classification consists of a system that makes use of Multilayer Perceptrons for those classification tasks in which speech is involved, and a Fisher Linear Discrimnant for distinguising stationary noisy environments from the non-stationary ones. The system performance has been found to be higher than that of other more classical approaches, and even superior than that of our preliminary work.