Playing Technique Classification Based on Deep Collaborative Learning of Variational Auto-Encoder and Gaussian Process

Sih Huei Chen, Yuan Shan Lee, Min Che Hsieh, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

Modeling musical timbre is critical for various music information retrieval (MIR) tasks. This work addresses the task of classifying playing techniques, which involves extremely subtle variations of timbre among different categories. A deep collaborative learning framework is proposed to represent a music with greater discriminative power than previously achieved. Firstly, a novel variational autoencoder (VAE) is developed to eliminate the variation of acoustic features within a class. Secondly, a Gaussian process classifier is jointly learned to distinguish the variations of timbres between classes, which increases the discriminative power of the learned representations. We derive a new lower bound that guides a VAE-based representation. Experiments were conducted on a database of seven classes of guitar playing techniques. The experimental results demonstrated that the proposed method outperforms baselines in terms of the Fl-score and accuracy.

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主出版物標題2018 IEEE International Conference on Multimedia and Expo, ICME 2018
發行者IEEE Computer Society
ISBN(電子)9781538617373
DOIs
出版狀態已出版 - 8 10月 2018
事件2018 IEEE International Conference on Multimedia and Expo, ICME 2018 - San Diego, United States
持續時間: 23 7月 201827 7月 2018

出版系列

名字Proceedings - IEEE International Conference on Multimedia and Expo
2018-July
ISSN(列印)1945-7871
ISSN(電子)1945-788X

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???event.eventtypes.event.conference???2018 IEEE International Conference on Multimedia and Expo, ICME 2018
國家/地區United States
城市San Diego
期間23/07/1827/07/18

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