Music instrument recognition: from isolated notes to solo phrases

Speech and audio processing techniques are used along with statistical pattern recognition principles to solve the problem of music instrument recognition. Non-temporal, frame level features only are used so that the proposed system is scalable from the isolated notes to the solo instrumental phrases scenario without the need for temporal segmentation of solo music. Based on their effectiveness in speech, line spectral frequencies (LSF) are proposed as features for music instrument recognition. The proposed system has also been evaluated using MFCC and LPCC features. Gaussian mixture models and K-nearest neighbour model classifier are used for classification. The experimental dataset included the Ulowa MIS and the C Music corporation RWC databases. Our best results at the instrument family level is about 95% and at the instrument level is about 90% when classifying 14 instruments.