Digital signal processing techniques for music structure analysis

UnrestrictedAutomatic music structure analysis from audio signals is an interesting topic that receives much attention these days. Its objective is to find the music structure by decomposing the music audio signals into sections and detect the repetitive parts. The technique will benefit music data analysis, indexing, retrieval and management. In this research, a three-level framework of music structure analysis is proposed. The first level is the beat level. Musical audio signals are analyzed via tempo analysis and the beat is derived as the basic temporal unit for each music piece. Then, feature vectors are extracted for each basic unit. The second level is the measure level, a similarity matrix between measures can be constructed based on multiple feature vectors in one measure. Then, the third level is the structure level, whose elements, for example, in a pop or rock song consists of the repetitive parts such as verses and choruses and the non-repetitive parts such as intro, outro and bridge. A technique based on dynamic programming is proposed to search similar parts of a song. With post-processing, the musical sections of the song can be extracted and their boundaries are estimated.; Many digital signal processing (DSP) techniques are proposed to address low-level music signal processing problems in the thesis. They are used to analyze the characteristics of musical audio signals. Specifically, techniques based on the phase-locked-loop (PLL), Kalman filter and the Hidden Markov Model (HMM) are developed for musical beat tracking. The beat locations are estimated on-line for the first two techniques while off-line for the last technique. To further tackle the incorrect measurements of beats, an enhanced probabilistic data association (PDA) that considers both information of prediction residual and music onsets' intensities is applied to original Kalman filter. On the other hand, for HMM-based musical beat tracking, a special state space is built to model the beats' periodic progression and Viterbi algorithm is used to estimate the beats' locations by decoding the musical audio signal into a sequence of beat states and non-beat states. Moreover, dynamic time warping (DTW) is used to calculate the optimized distance between two segments of music signals and thus helps building the measure-level similarity matrix. Finally, the measure-level similarity matrix is analyzed and repetitive parts of a song such as verses and choruses are identified via dynamic-programming-based technique. These are pioneering efforts in the music signal processing field, which appears to be a new frontier in digital signal processing