Music retrieval based on rhythm content and dynamic time warping method

Rhythm information, which plays an important role in music features, still has a long way to go. Most current researches on this field are based on single feature, which is unstable. In this paper, we proposed a novel method to change this by fusing rhythm feature with gammatone frequency cepstral coefficients(GFCC) feature. After the pre-processing including detecting the beginning of songs, removing the silence part using Energy and Zero crossing rate, our training and testing features are generated by fusing rhythm feature including pitch, tempo etc. with GFCC feature. Furthermore, we present several ways to measure the rhythmic similarity between two or more songs. This allows similar songs to be retrieved from a large collection. For recognition, we choose the Dynamic Time Warping(DTW) algorithm calculating the distance between test music and music database, and then we get a ranking list based on distance. It is demonstrated that we can improve the recognition rates by 21.3% on average based on our music database by using rhythm features fused with GFCC features comparing with Predominant melody, MFCC and GFCC features. Our music database has 500 songs and we choose 100 songs as testing music.