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Machine Learning and Signal Processing Based Acoustic Sensors

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

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 11030

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Special Issue Editor

Dr. Guangming Zhang

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Guest Editor

School of Engineering, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: micro- and nano-non destructive evaluation; 3D acoustic micro-imaging; prognostics and health management of electronics; ultrasonic signal/image processing; embeded acoustical sensors; reliability testing, failure analysis and fatigue life prediction of modern microelectronic packages

Special Issue Information

Dear Colleagues,

Acoustic detection is an important information-accessing technique and has widespread applications in a large variety of civil and military activities, including environmental noise monitoring, early warning of natural disasters, detection and tracking of unmanned aerial vehicles (UAVs), underwater detection, oil and gas pipeline leakage monitoring, wind turbine measurements, photoacoustic imaging, health monitoring, and so on. The traditional acoustic sensor is difficult to apply in harsh environments, and the maintenance cost is so high that it cannot meet the actual needs of modern engineering measurement. With the development of the acoustic sensing field, in order to improve cost-effectiveness and anti-electromagnetic interference capability, optical fiber acoustic sensing technology has been extensively studied.

This Special Issue will focus on the current state-of-the-art optical fiber acoustic sensors systems and their applications. We would like to invite researchers to contribute original papers as well as review articles showing breakthroughs and innovative advancements in the performance of optical fiber acoustic sensors systems in areas such as sensing range, strain sensitivity, frequency range, and spatial resolution.

Dr. Guangming Zhang
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.

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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 sensors
optical sensors
optical fiber sensors
optical fiber acoustic sensors
distributed optical fiber sensors
distributed acoustic sensing systems
hybrid optical–acoustic sensors
micro- and nano-sensors
embedded acoustic sensors
smart acoustic sensors
acoustic micro-imaging
nano-acoustics
machine learning and artificial intelligence
acoustic signal processing
multi-sensor data fusion
statistical pattern recognition
prognostics and health management of electronics

Published Papers (6 papers)

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Research

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17 pages, 1125 KiB

Open AccessArticle

Investigations on the Optimal Estimation of Speech Envelopes for the Two-Stage Speech Enhancement

by Yanjue Song and Nilesh Madhu

Sensors 2023, 23(14), 6438; https://doi.org/10.3390/s23146438 - 16 Jul 2023

Viewed by 796

Abstract

Using the source-filter model of speech production, clean speech signals can be decomposed into an excitation component and an envelope component that is related to the phoneme being uttered. Therefore, restoring the envelope of degraded speech during speech enhancement can improve the intelligibility and quality of output. As the number of phonemes in spoken speech is limited, they can be adequately represented by a correspondingly limited number of envelopes. This can be exploited to improve the estimation of speech envelopes from a degraded signal in a data-driven manner. The improved envelopes are then used in a second stage to refine the final speech estimate. Envelopes are typically derived from the linear prediction coefficients (LPCs) or from the cepstral coefficients (CCs). The improved envelope is obtained either by mapping the degraded envelope onto pre-trained codebooks (classification approach) or by directly estimating it from the degraded envelope (regression approach). In this work, we first investigate the optimal features for envelope representation and codebook generation by a series of oracle tests. We demonstrate that CCs provide better envelope representation compared to using the LPCs. Further, we demonstrate that a unified speech codebook is advantageous compared to the typical codebook that manually splits speech and silence as separate entries. Next, we investigate low-complexity neural network architectures to map degraded envelopes to the optimal codebook entry in practical systems. We confirm that simple recurrent neural networks yield good performance with a low complexity and number of parameters. We also demonstrate that with a careful choice of the feature and architecture, a regression approach can further improve the performance at a lower computational cost. However, as also seen from the oracle tests, the benefit of the two-stage framework is now chiefly limited by the statistical noise floor estimate, leading to only a limited improvement in extremely adverse conditions. This highlights the need for further research on joint estimation of speech and noise for optimum enhancement. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing Based Acoustic Sensors)

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14 pages, 4297 KiB

Open AccessArticle

Proposals for Surmounting Sensor Noises

by Andre Pittella and Timothy Sands

Sensors 2023, 23(6), 3169; https://doi.org/10.3390/s23063169 - 16 Mar 2023

Viewed by 952

Abstract

Classical and optimal control architectures for motion mechanics in the presence of noisy sensors use different algorithms and calculations to perform and control any number of physical demands, to varying degrees of accuracy and precision in regards to the system meeting the desired end state. To circumvent the deleterious effects of noisy sensors, a variety of control architectures are suggested, and their performances are tested for the purpose of comparison through the means of a Monte Carlo simulation that simulates how different parameters might vary under noise, representing real-world imperfect sensors. We find that improvements in one figure of merit often come at a cost in the performance in the others, especially depending on the presence of noise in the system sensors. If sensor noise is negligible, open-loop optimal control performs the best. However, in the overpowering presence of sensor noise, using a control law inversion patching filter performs as the best replacement, but has significant computational strain. The control law inversion filter produces state mean accuracy matching mathematically optimal results while reducing deviation by 36%. Meanwhile, rate sensor issues were more strongly ameliorated with 500% improved mean and 30% improved deviation. Inverting the patching filter is innovative but consequently understudied and lacks well-known equations to use for tuning gains. Therefore, such a patching filter has the additional drawback of having to be tuned through trial and error. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing Based Acoustic Sensors)

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13 pages, 2266 KiB

Open AccessArticle

Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions

by Horia Alexandru Modran, Tinashe Chamunorwa, Doru Ursuțiu, Cornel Samoilă and Horia Hedeșiu

Sensors 2023, 23(2), 986; https://doi.org/10.3390/s23020986 - 14 Jan 2023

Cited by 10 | Viewed by 4263

Abstract

Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. The current paper focuses on identifying and predicting whether music has therapeutic benefits. A machine learning model is developed, using a multi-class neural network to classify emotions into four categories and then predict the output. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person’s musical and emotional characteristics but is also trained to consider solfeggio frequencies. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing Based Acoustic Sensors)

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16 pages, 6095 KiB

Open AccessArticle

Small UAS Online Audio DOA Estimation and Real-Time Identification Using Machine Learning

by Alexandros Kyritsis, Rodoula Makri and Nikolaos Uzunoglu

Sensors 2022, 22(22), 8659; https://doi.org/10.3390/s22228659 - 09 Nov 2022

Cited by 4 | Viewed by 1835

Abstract

The wide range of unmanned aerial system (UAS) applications has led to a substantial increase in their numbers, giving rise to a whole new area of systems aiming at detecting and/or mitigating their potentially unauthorized activities. The majority of these proposed solutions for countering the aforementioned actions (C-UAS) include radar/RF/EO/IR/acoustic sensors, usually working in coordination. This work introduces a small UAS (sUAS) acoustic detection system based on an array of microphones, easily deployable and with moderate cost. It continuously collects audio data and enables (a) the direction of arrival (DOA) estimation of the most prominent incoming acoustic signal by implementing a straightforward algorithmic process similar to triangulation and (b) identification, i.e., confirmation that the incoming acoustic signal actually emanates from a UAS, by exploiting sound spectrograms using machine-learning (ML) techniques. Extensive outdoor experimental sessions have validated this system’s efficacy for reliable UAS detection at distances exceeding 70 m. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing Based Acoustic Sensors)

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Review

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29 pages, 4887 KiB

Open AccessReview

Augmented Hearing of Auditory Safety Cues for Construction Workers: A Systematic Literature Review

by Khang Dang, Kehinde Elelu, Tuyen Le and Chau Le

Sensors 2022, 22(23), 9135; https://doi.org/10.3390/s22239135 - 24 Nov 2022

Cited by 3 | Viewed by 2033 | Correction

Abstract

Safety-critical sounds at job sites play an essential role in construction safety, but hearing capability is often declined due to the use of hearing protection and the complicated nature of construction noise. Thus, preserving or augmenting the auditory situational awareness of construction workers has become a critical need. To enable further advances in this area, it is necessary to synthesize the state-of-the-art auditory signal processing techniques and their implications for auditory situational awareness (ASA) and to identify future research needs. This paper presents a critical review of recent publications on acoustic signal processing techniques and suggests research gaps that merit further research for fully embracing construction workers’ ASA of hazardous situations in construction. The results from the content analysis show that research on ASA in the context of construction safety is still in its early stage, with inadequate AI-based sound sensing methods available. Little research has been undertaken to augment individual construction workers in recognizing important signals that may be blocked or mixed with complex ambient noise. Further research on auditory situational awareness technology is needed to support detecting and separating important acoustic safety cues from complex ambient sounds. More work is also needed to incorporate context information into sound-based hazard detection and to investigate human factors affecting the collaboration between workers and AI assistants in sensing the safety cues of hazards. Full article

(This article belongs to the Special Issue Machine Learning and Signal Processing Based Acoustic Sensors)

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