Probabilistic framework for template-based chord recognition

This paper describes a method for chord recognition from audio signals. Our method provides a coherent and relevant probabilistic framework for template-based transcription. The only information needed for the transcription is the definition of the chords : in particular neither annotated audio data nor music theory knowledge is required. We extract from the signal a succession of chroma vectors which are our model observations. We propose a generative model for these observations from chord distribution probabilities and fixed chord templates. The parameters are evaluated through an EM algorithm. In order to capture the temporal structure, we apply some post-processing filtering methods before detecting the chords. Our method is evaluated on two audio corpus. Results show that our method outperforms state-of-the-art chord recognition methods and also gives more relevant chord transcriptions.