Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
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
Prediction-driven computational auditory scene analysis
Prediction-driven computational auditory scene analysis
Pattern Recognition and Prediction with Applications to Signal Processing (Aip Series in Modern Acoustics and Signal Processing)
Multi-pitch and periodicity analysis model for sound separation and auditory scene analysis
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Hierarchical classification of audio data for archiving and retrieving
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Foreground auditory scene analysis for hearing aids
Pattern Recognition Letters
Automatic Detection of Learnability under Unreliable and Sparse User Feedback
Proceedings of the 30th DAGM symposium on Pattern Recognition
EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
Low-complexity F0-based speech/nonspeech discrimination approach for digital hearing aids
Multimedia Tools and Applications
Recognition of hearing needs from body and eye movements to improve hearing instruments
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
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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A sound classification system for the automatic recognition of the acoustic environment in a hearing aid is discussed. The system distinguishes the four sound classes "clean speech," "speech in noise," "noise," and "music." A number of features that are inspired by auditory scene analysis are extracted from the sound signal. These features describe amplitude modulations, spectral profile, harmonicity, amplitude onsets, and rhythm. They are evaluated together with different pattern classifiers. Simple classifiers, such as rule-based and minimum-distance classifiers, are compared with more complex approaches, such as Bayes classifier, neural network, and hidden Markov model. Sounds from a large database are employed for both training and testing of the system. The achieved recognition rates are very high except for the class "speech in noise." Problems arise in the classification of compressed pop music, strongly reverberated speech, and tonal or fluctuating noises.