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Abstract
Self-supervised audio representation learning offers an attractive alternative for obtaining generic audio embeddings, capable to be employed into various downstream tasks. Published approaches that consider both audio and words/tags associated with audio do not employ text processing models that are capable to generalize to tags unknown during training. In this work we propose a method for learning audio representations using an audio autoencoder (AAE), a general word embed-dings model (WEM), and a multi-head self-attention (MHA) mechanism. MHA attends on the output of the WEM, pro-viding a contextualized representation of the tags associated with the audio, and we align the output of MHA with the out-put of the encoder of AAE using a contrastive loss. We jointly optimize AAE and MHA and we evaluate the audio representations (i.e. the output of the encoder of AAE) by utilizing them in three different downstream tasks, namely sound, music genre, and music instrument classification. Our results show that employing multi-head self-attention with multiple heads in the tag-based network can induce better learned audio representations.
Original language | English |
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Title of host publication | 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Publisher | IEEE |
Pages | 596-600 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-7281-7605-5 |
ISBN (Print) | 978-1-7281-7606-2 |
DOIs | |
Publication status | Published - 2021 |
Publication type | A4 Article in conference proceedings |
Event | IEEE International Conference on Acoustics, Speech and Signal Processing - Metro Toronto Convention Centre, Toronto, Canada Duration: 6 Jun 2021 → 11 Jun 2021 https://2021.ieeeicassp.org |
Publication series
Name | Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing |
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ISSN (Print) | 1520-6149 |
ISSN (Electronic) | 2379-190X |
Conference
Conference | IEEE International Conference on Acoustics, Speech and Signal Processing |
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Country/Territory | Canada |
City | Toronto |
Period | 6/06/21 → 11/06/21 |
Internet address |
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