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Intelligent Acoustic Sensors and Its Applications
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Intelligent Acoustic Sensors and Its Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 8006

Special Issue Editor

Prof. Dr. Cheng-Yuan Chang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Chung Yuan Christian University, Chung Li, Taoyuan 32023, Taiwan
Interests: real-time digital signal processing; sensors and measurements; active noise control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Acoustic sensors are built to measure an environment and convert this information into a digital or analog data signal that can be interpreted by a computer or observer. Much like a highly sensitive ear, acoustic sensors interpret the material’s voice into feasible waveforms. Additionally, acoustic sensors are a vital link in determining how to apply algorithms to achieve their purpose. Incorrect or improper installations of acoustic sensors result in the failure to meet projects or specific needs.

The scope of this Special Issue extends to all types of acoustic sensors with intelligent algorithms designed for productive applications.

Prof. Dr. Cheng-Yuan Chang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • acoustic sensor
  • sensor placement
  • virtual sensing
  • active noise control
  • acoustic noise reduction
  • speech enhancement
  • audio signal processing
  • acoustical modeling and simulation

Published Papers (2 papers)

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Research

17 pages, 1244 KiB  
Article
Sound Source Localization Using a Convolutional Neural Network and Regression Model
by Tan-Hsu Tan, Yu-Tang Lin, Yang-Lang Chang and Mohammad Alkhaleefah
Sensors 2021, 21(23), 8031; https://0-doi-org.brum.beds.ac.uk/10.3390/s21238031 - 01 Dec 2021
Cited by 15 | Viewed by 3986
Abstract
In this research, a novel sound source localization model is introduced that integrates a convolutional neural network with a regression model (CNN-R) to estimate the sound source angle and distance based on the acoustic characteristics of the interaural phase difference (IPD). The IPD [...] Read more.
In this research, a novel sound source localization model is introduced that integrates a convolutional neural network with a regression model (CNN-R) to estimate the sound source angle and distance based on the acoustic characteristics of the interaural phase difference (IPD). The IPD features of the sound signal are firstly extracted from time-frequency domain by short-time Fourier transform (STFT). Then, the IPD features map is fed to the CNN-R model as an image for sound source localization. The Pyroomacoustics platform and the multichannel impulse response database (MIRD) are used to generate both simulated and real room impulse response (RIR) datasets. The experimental results show that an average accuracy of 98.96% and 98.31% are achieved by the proposed CNN-R for angle and distance estimations in the simulation scenario at SNR = 30 dB and RT60 = 0.16 s, respectively. Moreover, in the real environment, the average accuracies of the angle and distance estimations are 99.85% and 99.38% at SNR = 30 dB and RT60 = 0.16 s, respectively. The performance obtained in both scenarios is superior to that of existing models, indicating the potential of the proposed CNN-R model for real-life applications. Full article
(This article belongs to the Special Issue Intelligent Acoustic Sensors and Its Applications)
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21 pages, 3617 KiB  
Article
Multirate Audio-Integrated Feedback Active Noise Control Systems Using Decimated-Band Adaptive Filters for Reducing Narrowband Noises
by Antonius Siswanto, Cheng-Yuan Chang and Sen M. Kuo
Sensors 2020, 20(22), 6693; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226693 - 23 Nov 2020
Cited by 2 | Viewed by 2961
Abstract
Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for [...] Read more.
Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments. Full article
(This article belongs to the Special Issue Intelligent Acoustic Sensors and Its Applications)
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