Music Features based on Hu Moments for Genre Classification

Automated musical genre classification using machine learning techniques has gained popularity for research and development of powerful tools to organize music collections available on web. Mel cepstral co-efficients (MFCC's) have been successfully used in music genre classification but they do not reflect the correlation between the adjacent co-efficients of Mel filters of a frame neither the relation between adjacent co-efficients of Mel filters of neighboring frames. This leads to loss of useful features. In this work, Hu moment based features are extracted from the spectrogram to study impact of energy concentration in the spectrogram. Under different musical genres the difference in rhythm in genres drastically changes the texture of spectrogram image. This alters the energy concentration in spectrogram. Hu moments being invariant to translation, scaling as well as rotation can capture useful features from spectrogram that are not considered by the MFCC's. Since the spectral moments are computed locally, they can assess the intensity of energy concentration at certain frequencies in spectrogram and prove as distinct features in characterizing different genres of music. Hu moment based features along with conventional music features lead to an accuracy of 83.33% for classifying 5 genres.