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A neurally inspired musical instrument classification system based upon the sound onset
Michael J Newton1, Leslie S Smith
1School of Music, University of Edinburgh, City of Edinburgh EH9 3JZ, United Kingdom. michael.newton@ed.ac.uk
The Journal of the Acoustical Society of America
|June 21, 2012
Summary
This study introduces an "onset fingerprint" descriptor for musical instrument classification, inspired by auditory system neurons. This method achieves high accuracy and demonstrates superior robustness on diverse datasets.
Area of Science:
- Auditory Neuroscience
- Computational Acoustics
- Machine Learning
Background:
- Sound onset detection is crucial for auditory processing and musical sound recognition.
- Specialized neurons in the cochlear nucleus may be involved in early sound onset detection.
- The sound onset is a key feature for identifying musical instruments.
Purpose of the Study:
- To develop a neurally inspired tone descriptor for musical instrument classification.
- To evaluate the effectiveness of an "onset fingerprint" using auditory system models.
- To compare the performance of the novel method against established techniques like mel-frequency cepstral coefficients.
Main Methods:
- Utilized a gammatone filterbank and spiking onset detectors (dynamic synapses, leaky integrate-and-fire neurons) to model auditory system response.
- Created an "onset fingerprint" descriptor emphasizing sound onsets.
- Employed an echo state network (time-domain neural network) for classification.
- Compared results with mel-frequency cepstral coefficients (MFCCs) evaluated on whole tones and onsets.
Main Results:
- The neurally inspired "onset fingerprint" method achieved approximately 75% classification success rate.
- Reference MFCC methods achieved classification rates between 73% and 76%.
- The "onset fingerprint" demonstrated significantly greater robustness when tested on an independent dataset (Iowa MIS collection).
Conclusions:
- The "onset fingerprint" effectively captures essential information from sound onsets for musical instrument classification.
- Neurally inspired auditory models can yield robust and competitive feature descriptors for audio analysis.
- The proposed method shows promise for applications requiring reliable instrument identification across different datasets.
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