Acoustic Signal Processing in the Age of AI: Methods, Hardware, and Impact

Dear Colleagues,

Advances in machine learning, edge/embedded computing, and cross-domain sensing have reinvented acoustic signal processing. This Special Issue gathers breakthroughs that transform how we analyze, enhance, localize, compress, synthesize, and interpret acoustic and vibroacoustic data—from speech and spatial audio to underwater, aeroacoustic, industrial, and ecological monitoring.

We welcome contributions that demonstrate robustness, latency/throughput gains, energy efficiency, interpretability, privacy, and scalability, beyond accuracy alone. Studies that unite signal-processing theory with modern learning (e.g., transformers, diffusion, neural ODE/SDEs), leverage distributed/federated sensing, or bridge acoustics with robotics, wearable/IoT, ecoacoustics, and smart-city infrastructures are especially encouraged. Real-world deployments, open datasets, and reproducible pipelines are strongly favored.

Article types: Original research, reviews, perspectives. Negative results with rigorous analysis, ablations, and open resources are welcome.

This Special Issue will focus on (but is not limited to) the following topics:

Foundations and methods

• Self-supervised, weakly/few-shot, and domain-adaptation methods for audio/ultrasound/sonar;
• Transformers, diffusion/generative models, neural ODE/SDEs; physics-informed and probabilistic models;
• Time–frequency representations, scattering/wavelet front-ends, robust beamforming and array processing;
• Graph/geometric learning for microphone/sonar networks; uncertainty quantification and calibration;
• Inverse problems, sparse/low-rank optimization, compressive and model-based deep learning.

Systems, hardware and edge

• TinyML, quantization/pruning/distillation; neuromorphic and spiking approaches;
• FPGA/ASIC/DSP implementations; scheduling for energy-aware, low-latency inference;
• Federated/split learning, privacy-preserving analytics; Internet of Acoustic Things (IoAT).

Applications

• Speech enhancement/separation/bandwidth extension; robust ASR; spatial and immersive audio (AR/VR);
• Bio/ecoacoustics across marine, terrestrial, and urban soundscapes; autonomous UUV/USV/UAV sensing;
• Underwater acoustics (passive/active), marine mammal/fish detection, geo/acoustic mapping;
• Aero/industrial/structural health monitoring; acoustic emission and vibroacoustics; HCI and assistive hearing.

Trustworthy and responsible acoustic AI

• Explainability (e.g., SHAP/LIME/attribution), fairness, safety under distribution shift, adversarial robustness;
• Data governance, annotation quality, simulation-to-real transfer, ethical considerations.

Data, benchmarking and reproducibility

• Open datasets, synthetic corpora, task protocols; evaluation beyond accuracy (latency, memory, joules/inference);
• Reproducible pipelines, MLOps for acoustic ML, standardized reporting and ablation practices.

Dr. Shashidhar Siddagangaiah
Dr. Jui-Hsiang Kao
Dr. Pai-Chen Guan
Guest Editors

Manuscript Submission Information

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