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Noise Measurement, Acoustic Signal Processing and Noise Control, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 1464

Editor


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Guest Editor
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China
Interests: sound-induced vibration; noise control; building acoustics; environmental noise measurement and control; sound source identification
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Special Issue Information

Dear Colleagues,

Noise measurement, acoustic signal processing, and noise control are all related fields, and they are very important for system analyses, experimental validation, and the manipulation of unwanted signals or noise. Noise measurement and signal processing involve quantifying the level and characteristics of a signal or noise and manipulating and interpreting signals in a system or environment. Acoustic signal processing also encompasses a wide range of techniques and algorithms that are used to extract meaningful information from signals, enhance signal quality, and remove or reduce noise. This processing can also involve multiple sensors in different locations and different algorithms. Noise control aims to reduce or eliminate unwanted noise to create a more desirable acoustic environment, based on noise measurements and signal processing. Noise control can be achieved through sound insulation, vibration isolation, active noise cancellation, sound reflections due to impedance mismatch or vibro-acoustic coupling, or the absorption of sound by porous materials, microperforated structures, or meta-materials. Therefore, the topic of noise measurement, acoustic signal processing, and noise control encompasses a range of multidisciplinary areas, including the development of technology, algorithm establishment, measurements, sensor arrangement, the signal processing of acoustics, vibration and machinery signals, signals from fluid dynamics, and the development and analysis of noise control technology. All papers on the technologies used for these purposes and their applications are welcome.

Dr. Yat Sze Choy
Guest Editor

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Keywords

  • sound source generation
  • sound source identification and characterization
  • signal processing
  • signal reconstruction
  • signal analysis
  • vibration signal analysis
  • automobile or vehicle noise
  • machinery noise
  • structural failure diagnoses
  • sensor arrangement
  • noise control
  • sound absorption
  • sound reflection
  • noise reduction

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Published Papers (2 papers)

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Research

18 pages, 4471 KB  
Article
2D-BiSpecNet: Bispectrum Image-Based Convolutional Network for Adaptive Subfilter Selection in Active Noise Control
by Laith Alsmadi, Noha Korany and Onsy Alim
Appl. Sci. 2026, 16(11), 5195; https://doi.org/10.3390/app16115195 - 22 May 2026
Viewed by 208
Abstract
Conventional adaptive active noise control (ANC) techniques, such as filtered-x normalized least mean square (FxNLMS), frequently run into issues when the noise environment changes, leading to longer reaction times. Moreover, fixed-filter approaches may lose the essential phase information necessary for efficient noise cancellation. [...] Read more.
Conventional adaptive active noise control (ANC) techniques, such as filtered-x normalized least mean square (FxNLMS), frequently run into issues when the noise environment changes, leading to longer reaction times. Moreover, fixed-filter approaches may lose the essential phase information necessary for efficient noise cancellation. This paper introduces 2D-BiSpecNet, a novel, effectively delayless feedforward active noise control system that uses a deep learning co-processor to address these difficulties. The technique converts one-dimensional audio signals into 64 × 64 bispectrum matrices, which transform sounds into visual representations. Therefore, it focuses on nonlinear quadratic phase couplings (QPCs), which provide robust and amplitude-independent views of the noise structure. The system then applies a quick multilabel classifier to examine these representations and immediately generates a control filter via 15 parallel subcontrol filters. The paper specifies a 5 × 5 convolutional receptive field that had the maximum efficacy. Simulations with real acoustic data indicate that this configuration yields an average noise reduction of −14.48 dB for aircraft noise, outperforming the usual FxNLMS technique by nearly 6 dB. The technology conducts classification and filtering nearly seven times faster than adaptive approaches, thus reducing convergence delays and delivering a more reliable and low-latency solution for noise-canceling environments. Full article
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16 pages, 5636 KB  
Article
Identification of Noise Tonality in the Proximity of Wind Turbines—A Case Study
by Wolniewicz Katarzyna and Zagubień Adam
Appl. Sci. 2026, 16(2), 734; https://doi.org/10.3390/app16020734 - 10 Jan 2026
Viewed by 873
Abstract
This paper presents a study of the tonality of sound emitted by a wind farm into the surrounding environment. The wind turbines installed at the site have a rated power of 3.0 MW. The aim of the study was to analyse the tonality [...] Read more.
This paper presents a study of the tonality of sound emitted by a wind farm into the surrounding environment. The wind turbines installed at the site have a rated power of 3.0 MW. The aim of the study was to analyse the tonality of sounds in the environment at the nearest residential area. The issue of tonal noise near the wind farm was identified during routine periodic noise monitoring. An experienced survey team identified the phenomenon and carried out preliminary field analyses. Detailed studies were then carried out to identify the environmental hazard and failure-free operation of the turbines. The recorded acoustic events are described in detail and an in-depth analysis is carried out. An action plan has been implemented in consultation with the wind farm operator to reduce tonal sound emissions to the surrounding environment. As a result of these interventions, tonal noise from the wind turbines was successfully reduced. It was determined that the detection of the potential tonality of the sounds emitted by wind turbines should take place during the analysis (active listening) of the .wav file, synchronised with Fast Fourier Transform (FFT) analysis. Conducting tonality assessments solely during field measurements may lead to incorrect identification of tonal sources. Full article
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