Deep Learning for Audio Signal Processing

TLDR

This review surveys state‑of‑the‑art deep‑learning techniques for audio signal processing and outlines key issues and future research questions. The authors compare speech, music, and environmental sound processing, survey dominant feature representations such as log‑mel spectra and raw waveforms, review deep‑learning models including CNNs, LSTM variants, and audio‑specific architectures, and discuss prominent application areas such as recognition, synthesis, and transformation.

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.