IEEE Journal of Selected Topics in Signal Processing· 2019Q1· Review
Deep Learning for Audio Signal Processing
- 837citations
- Q1SCImago
- 2019year
Short summary
This review surveys deep learning techniques applied to speech, music, and environmental sound processing, highlighting common methods, challenges, and cross-domain potential.
AI-generated from the title and abstract; the full text is not read.
Key points
- Deep learning techniques are reviewed for speech, music, and environmental sound processing.
- Dominant feature representations (log-mel spectra, raw waveform) and models (CNNs, LSTMs) are discussed.
- Applications include audio recognition (ASR, MIR, sound detection) and synthesis/transformation (source separation, enhancement, generation).
- Similarities, differences, and cross-fertilization potential between audio domains are highlighted.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. IEEE Journal of Selected Topics in Signal Processing, 2019 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science