Acoustics Sensing, Signal Processing, and Deep Learning Models

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 618

Special Issue Editors


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Guest Editor
Department of Systems Engineering and Naval Architecture, National Taiwan Ocean University, Keelung City 202301, Taiwan
Interests: acoustics and acoustic engineering; acoustic analysis; underwater acoustics; chaos theory; nonlinear dynamics; deep learning; machine learning; signal processing; audio signal processing

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Guest Editor
Department of Systems Engineering and Naval Architecture, National Taiwan Ocean University, Keelung City 202301, Taiwan
Interests: vibration and noise in ship structures; signal processing; vibration and noise in railway systems

E-Mail Website
Guest Editor
Department of Systems Engineering and Naval Architecture, National Taiwan Ocean University, Keelung City 202301, Taiwan
Interests: propeller vibration and noise; ship propulsion systems; wind power generation system integration; industrial fan design

Special Issue Information

Dear Colleagues,

Acoustics has emerged as the key to sensing in a wide range of applications, from space to deep sea environments. The development of acoustic devices ranges from sophisticated consumer electronics to advanced acoustic sensing in ultrasonic, marine, and terrestrial environments, and space. These diverse technological applications of acoustic sensing have given way to the rapid process of novel signal processing and methodologies to analyze acoustic data, ranging from traditional methods to recent applications of deep learning and generative models for voice recognition, voice-to-speech conversion, and fake audio. This Special Issue aims to publish these recent technological advances in acoustic sensing and related methodologies.

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:

  • Marine and Terrestrial Acoustics.
  • Space Acoustics.
  • Room Acoustics.
  • Vibrational Acoustics.
  • Electronic Acoustics.
  • Deep learning and Generative Models in Acoustics.
  • Signal Processing Methods in Acoustics.
  • Acoustics in Medical Research.
  • Underwater Acoustic Communication.
  • Acoustical Time Series Analysis.
  • Nonlinear Methodologies in Acoustics.

Dr. Shashidhar Siddagangaiah
Prof. Dr. Rong-Juin Shyu
Prof. Dr. Jui-Hsiang Kao
Guest Editors

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Keywords

  • acoustics
  • signal processing
  • audio
  • deep learning in audio

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Published Papers (1 paper)

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Research

19 pages, 10481 KiB  
Article
Low-Frequency Active Noise Control System Based on Feedback FXLMS
by Ting-Yu Chen, Jia-Horng Yang, Chien-Liang Lai and Chun-Ta Wei
Electronics 2025, 14(7), 1442; https://doi.org/10.3390/electronics14071442 - 2 Apr 2025
Viewed by 366
Abstract
The rise of industrial machinery and military training activities has significantly contributed to low-frequency noise pollution, which can penetrate traditional passive noise isolation methods and pose serious health risks, including irreversible hearing damage. To address this challenge, this study proposes a hybrid active [...] Read more.
The rise of industrial machinery and military training activities has significantly contributed to low-frequency noise pollution, which can penetrate traditional passive noise isolation methods and pose serious health risks, including irreversible hearing damage. To address this challenge, this study proposes a hybrid active noise control (HANC) system, integrating an adaptive active noise control (AANC) module based on the filtered-x least mean squares (FxLMS) algorithm and an audio-balance control circuit (ABCC). The AANC system actively generates anti-noise signals to mitigate low-frequency disturbances, while the ABCC module enhances voice clarity and protects users from excessive impulse noise. MATLAB R2023b simulations and hardware implementations validate the system’s effectiveness, achieving a noise reduction of up to 21.8 dB in controlled environments. Additionally, the proposed feedback active noise control architecture ensures robust performance under dynamic noise conditions, improving stability and response time. By integrating both software-based adaptive filtering and hardware circuit design, this study provides a comprehensive noise mitigation solution with potential applications in military, industrial, and vehicular environments. Full article
(This article belongs to the Special Issue Acoustics Sensing, Signal Processing, and Deep Learning Models)
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