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Intelligent Engineering Systems through Artificial Neural Networks, Volume 16

Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

The purpose of this paper is to classify automatically musical instrument sounds on the basis of a limited number of parameters. And this involves issues like feature extraction and development of classifier using the obtained features. As for feature extraction, a 5 second audio file stored in WAVE format is passed to a feature extraction function. The feature extraction function calculates more than 20 numerical features both in time-domain and frequency-domain that characterize the sample. Regarding the task of classification, we designed a two-layer Feed-Forward Neural Network (FFNN) using back-propagation training algorithm. The FFNN is trained in a supervised manner...

Abstract
1. Introduction
2. The Database
3. Preprocessing
4. Feature Extraction
5. Results of Automatic Classification Training Phase
6. Testing Phase
7. Feature Vector Selection
8. Conclusion
Acknowledgements
References
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