Efficient Musical Instrument Recognition on Solo Performance Music using Basic Features
Musical instrument recognition has gained growing concern for the promise it holds towards advances in musical content description. The present study pursues the goal of showing the efficiency of some basic features for such a recognition task in the realistic situation where solo musical phrases are played. A large and varied database of sounds assembled from different commercial recordings is used to ensure better training and testing conditions, in terms of statistical efficiency. It is found that when combining cepstral features with others describing the audio signal spectral shape, a high recognition accuracy can be achieved in association with Support Vector Machine classification (especially when using a Radial Basis Function kernel).
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