mlsanityData to 7 Oct 2026

Machine learning in acoustics: theory and applications

arXiv 1905.04418 ↗First posted 11 May 2019eess.SP cs.LG cs.SDcited by 36

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Abstract (arXiv)

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 5; 2023: 8; 2024: 3; 2025: 6; 2026: 4.

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  1. 2021A Survey of Sound Source Localization with Deep Learning Methodscited by 45
  2. 2022Improving aircraft performance using machine learning: a reviewcited by 19
  3. 2022Sound Localization by Self-Supervised Time Delay Estimationcited by 18
  4. 2023Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responsescited by 8
  5. 2023Reconstruction of Sound Field through Diffusion Modelscited by 8
  6. 2022A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacousticcited by 7
  7. 2023The Galerkin method beats Graph-Based Approaches for Spectral Algorithmscited by 6
  8. 2021Semi-supervised source localization in reverberant environments with deep generative modelingcited by 5
  9. 2023Generative adversarial networks with physical sound field priorscited by 4
  10. 2023Learning to Predict Structural Vibrationscited by 4
  11. 2021A Data-Driven Approach to Violin Makingcited by 2
  12. 2021Echo-Reconstruction: Audio-Augmented 3D Scene Reconstructioncited by 2
24 more
  1. 2023ElectrodeNet -- A Deep Learning Based Sound Coding Strategy for Cochlear Implantscited by 2
  2. 2024ISR: Invertible Symbolic Regressioncited by 2
  3. 2025A data-driven two-microphone method for in-situ sound absorption measurementscited by 2
  4. 2021Acoustic Structure Inverse Design and Optimization Using Deep Learningcited by 1
  5. 2021Audio scene monitoring using redundant ad-hoc microphone array networkscited by 1
  6. 2021Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Networkcited by 1
  7. 2021SofaMyRoom: a fast and multiplatform "shoebox" room simulator for binaural room impulse response dataset generationcited by 1
  8. 2021A Machine-learning Framework for Acoustic Design Assessment in Early Design Stagescited by 1
  9. 2022Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approachcited by 1
  10. 2024Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphonescited by 1
  11. 2024Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Datacited by 1
  12. 2025A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methodscited by 1
  13. 2021Expert Decision Support System for aeroacoustic source type identification using clusteringcited by 0
  14. 2022Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracycited by 0
  15. 2023An experiment on an automated literature survey of data-driven speech enhancement methodscited by 0
  16. 2023ML-ASPA: A Contemplation of Machine Learning-based Acoustic Signal Processing Analysis for Sounds, & Strains Emerging Technologycited by 0
  17. 2025VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identificationcited by 0
  18. 2025Prediction of acoustic field in 1-D uniform duct with varying mean flow and temperature using neural networkscited by 0
  19. 2025HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Wavescited by 0
  20. 2025Graph Embedding with Mel-spectrograms for Underwater Acoustic Target Recognitioncited by 0
  21. 2026Total Generalized Variation regularization closes the gap between neural-eld and classical methods in seismic travel-time tomographycited by 0
  22. 2026Rhythm of the Deep: Two-tier acoustic organization of sperm-whale codas from click waveforms to second-order sequence dependencecited by 0
  23. 2026A Data-Efficient Analytical Prior Machine Learning Framework for Sound Reduction Frequency Prediction in Helmholtz Resonatorscited by 0
  24. 2026Mitigating Spectral Bias in Neural Operators for Underwater Transmission Loss Predictioncited by 0