# Deep Learning for Audio Signal Processing

arXiv: https://arxiv.org/abs/1905.00078 · HTML: https://mlsanity.com/paper/1905.00078/

## Key facts

- Cited by 92 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 30 of 92)

- [Transformer for Graphs: An Overview from Architecture Perspective](https://mlsanity.com/paper/2202.08455/) (2022)
- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis](https://mlsanity.com/paper/2310.20496/) (2023)
- [A Light Weight Model for Active Speaker Detection](https://mlsanity.com/paper/2303.04439/) (2023)
- [Contrastive Domain Adaptation](https://mlsanity.com/paper/2103.15566/) (2021)
- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [VkD: Improving Knowledge Distillation using Orthogonal Projections](https://mlsanity.com/paper/2403.06213/) (2024)
- [Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition](https://mlsanity.com/paper/2206.03393/) (2022)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [Explainable AI for Time Series via Virtual Inspection Layers](https://mlsanity.com/paper/2303.06365/) (2023)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications](https://mlsanity.com/paper/2110.13290/) (2021)
- [Efficient Domain Adaptation for Speech Foundation Models](https://mlsanity.com/paper/2302.01496/) (2023)
- [Hypernetworks build Implicit Neural Representations of Sounds](https://mlsanity.com/paper/2302.04959/) (2023)
- [LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms](https://mlsanity.com/paper/2311.11420/) (2023)
- [Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression](https://mlsanity.com/paper/2107.04386/) (2021)
- [Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines](https://mlsanity.com/paper/2202.08679/) (2022)
- [Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework](https://mlsanity.com/paper/2306.11229/) (2023)
- [Piecewise Linear Neural Networks and Deep Learning](https://mlsanity.com/paper/2206.09149/) (2022)
- [Detection of Audio-Video Synchronization Errors Via Event Detection](https://mlsanity.com/paper/2104.10116/) (2021)
- [Neural Score Matching for High-Dimensional Causal Inference](https://mlsanity.com/paper/2203.00554/) (2022)
- [SAV-SE: Scene-aware Audio-Visual Speech Enhancement with Selective State Space Model](https://mlsanity.com/paper/2411.07751/) (2024)
- [Semi-supervised source localization in reverberant environments with deep generative modeling](https://mlsanity.com/paper/2101.10636/) (2021)
- [Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices](https://mlsanity.com/paper/2103.03483/) (2021)
- [Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients](https://mlsanity.com/paper/2104.08954/) (2021)
- [Conditional Generative Data Augmentation for Clinical Audio Datasets](https://mlsanity.com/paper/2203.11570/) (2022)
- [JaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus](https://mlsanity.com/paper/2211.16028/) (2022)
- [Teaching What You Should Teach: A Data-Based Distillation Method](https://mlsanity.com/paper/2212.05422/) (2022)

## Abstract (arXiv)

Given the recent surge in developments of deep learning, this article 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.