Recognition of Indian Musical Instruments with Multi-Classifier Fusion

Multiple Classifier fusion is an efficient and widely useful method of improving system performance. The classifier fusion approach to musical instrument recognition system is not been widely experimented. This paper explores in depth a classifier combination approach for the instrument classification task, studied over a diverse classifier pool, which includes K-Nearest Neighbor, Gaussian Mixture Model and Multi-Layer Perceptron classifiers. All three classifiers were trained with the same feature space, comprised of spectral, temporal, harmonic, perceptual and statistical features. The classifier fusion has been done at decision level. We employ the Sum-based and Confidence-based integration strategies to combine three classifiers k-NN, MLP and GMM. Experiments conducted on a musical sound database containing 10 different Indian musical instruments sounds prove that the proposed classifier combination approaches outperform individual classifiers.