Download Chroma and MFCC Based Pattern Recognition in Audio Files Utilizing Hidden Markov Models And Dynamic Programming In this paper we present an algorithm to reveal the immanent musical structure within pieces of popular music. Our proposed model uses an estimate of the harmonic progression which is obtained by calculating beat-synchronous chroma vectors and letting a Hidden Markov Model (HMM) decide the most probable sequence of chords. In addition, MFCC vectors are computed to retrieve basic timbral information that can not be described by harmony. Subsequently, a dynamic programming algorithm is used to detect repetitive patterns in these feature sequences. Based on these patterns a second dynamic programming stage tries to find and link corresponding patterns to larger segments that reflect the musical structure.
Download Music Structure Discovery Based on Normalized Cross Correlation Music Structure Discovery (MSD) for popular music is a well known task in Music Information Retrieval (MIR). The proposed approach tries to find the basic musical structure of a piece of music, by applying a template matching algorithm on a modified, bar level Self Distance Matrix (SDM). Mel frequency cepstral coefficients (MFCC) are used to represent timbral qualities of the audio material while chroma vectors are selected to incorporate pitch and harmonic content. The new idea of template matching instead of trying to find explicit blocks or off-diagonal lines is independent of any specific characteristics of the underlying SDM and can therefore be used on a wide range of different songs.