Persian Classical Music Instrument Recognition (PCMIR) Using a Novel Persian Music Database

Audio signal classification is an important field in pattern recognition and signal processing. Classification of musical instruments is a branch of audio signal classification and poses unique challenges due to the diversity of available instruments. Automatic expert systems could assist or be used as a replacement for humans. The aim of this work is to classify Persian musical instruments using combination of extracted features from audio signal. We believe such an automatic system to recognize Persian musical instruments could be very useful in an educational context as well as art universities. Features like Mel-Frequency Cepstrum Coefficients (MFCCs), Spectral Roll-off, Spectral Centroid, Zero Crossing Rate and Entropy Energy are employed and work well for this purpose. These features are extracted from audio signals out of our novel database. This database contains audio samples for 7 Persian musical instrument classes: Ney, Tar, Santur, Kamancheh, Tonbak, Ud and Setar. In feature selection part, Fuzzy entropy measure is employed and classification task takes place by Multi-Layer Neural Network (MLNN). It should be mentioned that this research is one of the first researches on Persian musical instrument classification. Validation confusion matrix made of true positive and false negative rates along with true and false observations numbers. Acquired results are so promising and satisfactory.