Structure Discovery of Pop Music Using HHMM E6820 Project Jessie Hsu 03/09/05

Problem Description Given Wav signal of a pop song Discover the structure of the song Intro Verse Chorus Bridge Outro

HMM Framework Model the music signal as a series of state transitions …… Observations Hidden States

HMM Framework: Hierarchical HMM …… Observations Hidden States at Frame Level IntroVerseOutro Hidden States at Structure Level Each observation is an audio frame of one beat length

Representing a HHMM HHMM parameters Prior of each state at structure level and frame level π State transition probabilities at structure level and frame level α Emission parameters for each state at both levels Each state is modeled as a mixture of Gaussians Mean μ and covariance matrices Σ of each Gaussian

Training a HHMM EM for HHMM Look for maximum likelihood state sequence and model parameters M-step: Best state sequence Backward-forward algorithm Viterbi algorithm E-step: Parameter estimation Priors at both levels π State transition probabilities α Emission parameters - Gaussian mixture mean μ and covariance matrices Σ

Preprocessing Beat detection Segment the music into beat-length frames Feature extraction Repetition related feature (chorus/nonchorus) – Chroma vector Intensity related feature (vocal/nonvocal) - Subband based Log Frequency Power Coefficients Pitch related features – narrowband spectrogram features (Hann windowed FFT coefficients) And possibly more….under investigation

Tasks HHMM on a test song Songs with I-V1-C1-V2-C2-(V3-C3)-B-O structure Manually label structures as ground truth Predefine the number of states at both structure and frame levels Preprocessing Model fitting Evaluation Accuracy of structure identification Accuracy of structure timing

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