Communication Dans Un Congrès Année : 2018

MAIN MELODY EXTRACTION WITH SOURCE-FILTER NMF AND CRNN

Résumé

Estimating the main melody of a polyphonic audio recording remains a challenging task. We approach the task from a classification perspective and adopt a convolutional recurrent neural network (CRNN) architecture that relies on a particular form of pretraining by source-filter nonneg-ative matrix factorisation (NMF). The source-filter NMF decomposition is chosen for its ability to capture the pitch and timbre content of the leading voice/instrument, providing a better initial pitch salience than standard time-frequency representations. Starting from such a musically motivated representation, we propose to further enhance the NMF-based salience representations with CNN layers , then to model the temporal structure by an RNN network and to estimate the dominant melody with a final classification layer. The results show that such a system achieves state-of-the-art performance on the MedleyDB dataset without any augmentation methods or large training sets.

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hal-02019103 , version 1 (14-02-2019)

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  • HAL Id : hal-02019103 , version 1

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Dogac Basaran, Slim Essid, Geoffroy Peeters. MAIN MELODY EXTRACTION WITH SOURCE-FILTER NMF AND CRNN. 19th International Society for Music Information Retreival, Sep 2018, Paris, France. ⟨hal-02019103⟩
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