Self-organizing maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sound-source recognition: a theory and computational model
Sound-source recognition: a theory and computational model
Editorial: Hybrid learning machines
Neurocomputing
Transcription and expressiveness detection system for violin music
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Speech, Audio, Image and Biomedical Signal Processing using Neural Networks
Speech, Audio, Image and Biomedical Signal Processing using Neural Networks
Logic-oriented neural networks for fuzzy neurocomputing
Neurocomputing
Information Sciences: an International Journal
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
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Audio signal classification consists of extracting some descriptive features from a sound and use them as input in a classifier. Then, the classifier will assign a different label to any different sound class. The classification of the features can be performed in a supervised or unsupervised way. However, unsupervised classification usually supposes a challenge against supervised classification as it has to be performed without any a priori knowledge. In this paper, unsupervised classification of audio signals is accomplished by using a Probabilistic Self-Organizing Map (PSOM) with probabilistic labeling. The hybrid unsupervised classifier presented in this work can achieve higher detection rates than the reached by the unsupervised traditional SOM. Moreover, real audio recordings from clarinet music are used to show the performance of our proposal.