Khadkevich, Maksim

Omologo, Maurizio

EURASIP Journal on Audio, Speech, and Music Processing

English

Most existing automatic chord recognition systems use a chromagram in front-end processing and some sort of classifier (e.g., hidden Markov model, Gaussian mixture model (GMM), support vector machine, or other template matching technique). The vast majority of front-end algorithms derive acoustic features based on a standard short-time Fourier analysis and on mapping energy from the power spectrum, or from a constant-Q spectrum, to chroma bins. However, the accuracy of the resulting spectral representation is a crucial issue. In fact, conventional methods based on short-time Fourier analysis involve an intrinsic trade-off between time resolution and frequency resolution. This work investigates an alternative feature set based on time-frequency reassignment, which was applied in the past to speech processing tasks such as formant extraction. As shown in the following experiments, the reassigned spectrum provides a very accurate front-end for the GMM-based chord recognition system here investigated

Archivio della ricerca - Fondazione Bruno Kessler

Reassigned spectrum-based feature extraction for GMM-based automatic chord recognition

Maksim Khadkevich

Maurizio Omologo

Springer - Publisher Connector

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Reassigned spectrum-based feature extraction for GMM-based automatic chord recognition

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Archivio della ricerca - Fondazione Bruno Kessler

Springer - Publisher Connector

Springer - Publisher Connector

Open Access Repository