Octave-dependent Probabilistic Latent Semantic Analysis to Chorus Detection of Popular Song

Content representation of music signal is an essential part of music information retrieval applications, e.g. chorus detection, genre classification, etc. In the paper, we propose the octave-dependent probabilistic latent semantic analysis (OdPlsa) to discover the latent audio patterns (or clusters) through spectral-temporal analysis. Then the audio content of each segment is characterized using the statistical pattern distribution. In OdPlsa, the latent pattern is modeled by multinomial distribution which characterizes the magnitude distribution of 12-dimensional pitch class profiles over a temporal window. It thus effectively models melody information as well as octave relations in music signal. Its efficiency as a feature extraction technique is evaluated on chorus detection of popular songs. In terms of multiple performance metrics such as boundary accuracy, precision, recall and F1, the proposed technique is much superior to the widely accepted chroma feature.