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Conference Papers Year : 2018

Sparse Coding of Pitch Contours with Deep Auto-Encoders

Nicolas Obin
Julie Beliao


This paper presents a sparse coding algorithm based on deep auto-encoders for the stylization and the clustering of pitch contours. The main objective of the proposed algorithm is to learn a set of pitch templates that can be easily interpreted by humans and whose combination can approximate efficiently the observed pitch contours. The proposed learning architecture is based on deep auto-encoders, commonly used to learn non-linear and low-dimensional latent representations that approximate the observed data. The proposed deep architecture is based on stacked auto-encoders and the sparsity of the network is investigated in order to learn a more robust and general representation of the pitch contours (dropout, denoising auto-encoder, sparsity regularization). The deep auto-encoding of the pitch contours is illustrated and discussed on the TIMIT American-English speech database † with comparison of other existing stylization and clustering algorithms.
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Dates and versions

hal-01722007 , version 1 (02-03-2018)



Nicolas Obin, Julie Beliao. Sparse Coding of Pitch Contours with Deep Auto-Encoders. Speech Prosody, Mar 2018, Poznan, Poland. pp.799-803, ⟨10.21437/SpeechProsody.2018-161⟩. ⟨hal-01722007⟩
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