Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Speaker-independent recognition of isolated words using clustering techniques
Readings in speech recognition
Comparison of techniques for environmental sound recognition
Pattern Recognition Letters
Information extraction from sound for medical telemonitoring
IEEE Transactions on Information Technology in Biomedicine
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In this paper we present a new approach for non-speech sound identification using Self Organizing Maps (SOM). We have found that by applying the SOM identification in a two-staged process, the identification rate surpasses the performance of other identification attempts. We have experimented with up to sixty different sounds maintaining a success rate of 70%. While even higher ratess have been reported by others, such high rates were typically limited to less than ten different sounds. We also found that as little as four sound-samples from each sound are sufficient for training the SOM. Non-speech environmental sound identification is different from speaker and music instrument identification in that it spans (in theory) an unbounded range of frequencies. This makes the pool of sound patterns almost limitless and consequently the pattern matching quite extensive and difficult.