Huang, Q. and Hain, T. orcid.org/0000-0003-0939-3464 (2020) Exploration of audio quality assessment and anomaly localisation using attention models. In: Meng, H., Xu, B. and Zheng, T., (eds.) Proceedings of Interspeech 2020. Interspeech 2020, 25-29 Oct 2020, Shanghai, China. ISCA - International Speech Communication Association , pp. 4611-4615.
Abstract
Many applications of speech technology require more and more audio data. Automatic assessment of the quality of the collected recordings is important to ensure they meet the requirements of the related applications. However, effective and high performing assessment remains a challenging task without a clean reference. In this paper, a novel model for audio quality assessment is proposed by jointly using bidirectional long short-term memory and an attention mechanism. The former is to mimic a human auditory perception ability to learn information from a recording, and the latter is to further discriminate interferences from desired signals by highlighting target related features. To evaluate our proposed approach, the TIMIT dataset is used and augmented by mixing with various natural sounds. In our experiments, two tasks are explored. The first task is to predict an utterance quality score, and the second is to identify where an anomalous distortion takes place in a recording. The obtained results show that the use of our proposed approach outperforms a strong baseline method and gains about 5% improvements after being measured by three metrics, Linear Correlation Coefficient and Spearman’s Rank Correlation Coefficient, and F1.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 ISCA. This is an author-produced version of a paper subsequently published in Proceedings of Interspeech 2020. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | quality assessment; attention model; anomaly localisation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Jul 2022 08:59 |
Last Modified: | 18 Jul 2022 15:38 |
Status: | Published |
Publisher: | ISCA - International Speech Communication Association |
Refereed: | Yes |
Identification Number: | 10.21437/interspeech.2020-1885 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189106 |