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Hierarchical disentangled representation learning for singing voice conversion | IEEE Conference Publication | IEEE Xplore

Hierarchical disentangled representation learning for singing voice conversion


Abstract:

Conventional singing voice conversion (SVC) methods often suffer from operating in high-resolution audio owing to a high dimensionality of data. In this paper, we propose...Show More

Abstract:

Conventional singing voice conversion (SVC) methods often suffer from operating in high-resolution audio owing to a high dimensionality of data. In this paper, we propose a hierarchical representation learning that enables the learning of disentangled representations with multiple resolutions independently. With the learned disentangled representations, the proposed method progressively performs SVC from low to high resolutions. Experimental results show that the proposed method outperforms baselines that operate with a single resolution in terms of mean opinion score (MOS), similarity score, and pitch accuracy.
Date of Conference: 18-22 July 2021
Date Added to IEEE Xplore: 20 September 2021
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Conference Location: Shenzhen, China

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