The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Deep Learning for Audio Signal Processing
Open accessAbstract
Given the recent surge in developments of deep learning, this paper provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e., audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.
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Cites
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Method
Cited by
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717
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Explainable AI for time series via Virtual Inspection Layers
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Sparks of Large Audio Models: A Survey and Outlook
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“While several comprehensive surveys delve into the applications of deep learning for audio processing [11, 28, 53, 54], including speech [25, 26], music [55–57], and other categories [58, 59], none concentrate on the advent and deployment of LLMs in this field.”
Background
Metadata
- Authors
- Purwins, H., Li, B., Virtanen, T., Schluter, J., Chang, S.-Y., & Sainath, T.
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2019-05
- Volume
- 13
- Issue
- 2
- Pages
- 206-219
- ISSN
- 1932-4553 (ISSN-L)
- 1941-0484
- OpenAlex
- W2931364255
- Cited by
- 855
- References
- 22
- FWCI
- 55.91
- Type
- journal article
- Language
- en
- Open access
- green
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