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Orbital angular momentum (OAM) multiplexing/demultiplexing technology is crucial in increasing the data transmission rate for acoustic communication. However, the existing acoustic OAM multiplexing/demultiplexing is still limited to eight channels, and its combination with other communication techniques has not been verified experimentally. Here, we experimentally demonstrate the demultiplexing of up to 40 data channels using OAM multiplexing combined with wavelength-division multiplexing (WDM). The proposed demultiplexing method is improved from the virtual rotating receiver method used to detect the rotational Doppler effect of the OAM waves by a static array of microphones. The improved method has overcome the challenges of insufficient response, cross-talk, and signal aliasing, which often hinder the existing demultiplexing methods in the low-frequency region. The proposed demultiplexing method can be used to quickly decode the massive information concealed in a large number of acoustic OAM-WDM channels. Our work also shows practical prospects in underwater communication applications, especially in long-range communication using acoustic waves at low frequencies. [ABSTRACT FROM AUTHOR]

The acoustic analysis of a moving object, such as in pass-by or fly-over tests, is a very important and demanding issue. These types of analyses make it possible to characterize the machine in quite realistic conditions, but the typical difficulties related to source localization and characterization are usually exacerbated by the need to take into consideration and to compensate for the object movement. In this paper, a technique based on acoustic beamforming is proposed, which is applicable to all those cases where the object under investigation is moving. In the proposed technique, the object's movement is not regarded as a problem but as a resource, enabling a virtual increase in the number of microphone acquisitions. For a stationary acoustic emission from a moving object, each time segment of the acquired signal is treated as if it is coming from a microphone (virtual) positioned differently relative to the object's reference system. This paper describes the technique and presents examples of results obtained from both simulated and real signals. Performance analysis is conducted and discussed in detail. [ABSTRACT FROM AUTHOR]

Characterising acoustic fields in rooms is challenging due to the complexity of data acquisition. Sound field reconstruction methods aim at predicting the acoustic quantities at positions where no data are available, incorporating generalisable physical priors of the sound in a room. This study introduces a model that exploits the general time structure of the room impulse response, where a wave-based expansion addresses the direct sound and early reflections, localising their apparent origin, and kernel methods are applied to the late part. This late energy is considered to follow a sinc-like spatial correlation, in accordance with the random wave field theory. Synthesised pressure points, which follow the observed statistics of the sound field, are introduced to enable extrapolation over large distances. The model is evaluated experimentally in a lecture room and an auditorium, demonstrating a successful reconstruction of the sound field across a 5 m aperture using three microphone arrays of only 4.2 cm radius each. These results indicate that the proposed methodology enables volumetric extrapolation over several orders of magnitude, which is significant in the context of navigable sound field reproduction, "6-degrees of freedom" spatial audio and sound field analysis in rooms. [ABSTRACT FROM AUTHOR]

In this study, we investigate the effectiveness of spatial features in acoustic scene classification using distributed microphone arrays. Under the assumption that multiple subarrays, each equipped with microphones, are synchronized, we investigate two types of spatial feature: intra- and inter-generalized cross-correlation phase transforms (GCC-PHATs). These are derived from channels within the same subarray and between different subarrays, respectively. Our approach treats the log-Mel spectrogram as a spectral feature and intra- and/or inter-GCC-PHAT as a spatial feature. We propose two integration methods for spectral and spatial features: (a) middle integration, which fuses embeddings obtained by spectral and spatial features, and (b) late integration, which fuses decisions estimated using spectral and spatial features. The evaluation experiments showed that, when using only spectral features, employing all channels did not markedly improve the F1-score compared with the single-channel case. In contrast, integrating both spectral and spatial features improved the F1-score compared with using only spectral features. Additionally, we confirmed that the F1-score for late integration was slightly higher than that for middle integration. [ABSTRACT FROM AUTHOR]

Multi-channel speech enhancement has become an active area of research, demonstrating excellent performance in recovering desired speech signals from noisy environments. Recent approaches have increasingly focused on leveraging spectral information from multi-channel inputs, yielding promising results. In this study, we propose a novel feature integration network that not only captures spectral information but also refines it through shifted-window-based self-attention, enhancing the quality and precision of the feature extraction. Our network consists of blocks containing a full- and sub-band LSTM module for capturing spectral information, and a global–local attention fusion module for refining this information. The full- and sub-band LSTM module integrates both full-band and sub-band information through two LSTM layers, while the global–local attention fusion module learns global and local attention in a dual-branch architecture. To further enhance the feature integration, we fuse the outputs of these branches using a spatial attention module. The model is trained to predict the complex ratio mask (CRM), thereby improving the quality of the enhanced signal. We conducted an ablation study to assess the contribution of each module, with each showing a significant impact on performance. Additionally, our model was trained on the SPA-DNS dataset using a circular microphone array and the Libri-wham dataset with a linear microphone array, achieving competitive results compared to state-of-the-art models. [ABSTRACT FROM AUTHOR]

To address the performance and computational complexity issues in speech enhancement for hearing aids, a speech enhancement algorithm based on a microphone array and a lightweight two-stage convolutional recurrent network (CRN) is proposed. The algorithm consists of two main modules: a beamforming module and a post-filtering module. The beamforming module utilizes directional features and a complex time-frequency long short-term memory (CFT-LSTM) network to extract local representations and perform spatial filtering. The post-filtering module uses analogous encoding and two symmetric decoding structures, with stacked CFT-LSTM blocks in between. It further reduces residual noise and improves filtering performance by passing spatial information through an inter-channel masking module. Experimental results show that this algorithm outperforms existing methods on the generated hearing aid dataset and the CHIME-3 dataset, with fewer parameters and lower model complexity, making it suitable for hearing aid scenarios with limited computational resources. [ABSTRACT FROM AUTHOR]

Based on the conventional beamforming (CBF), some algorithms for far-field sound source identification have been proposed in the past few decades. Typically, the functional beamforming (FBF) and the deconvolution methods, such as CLEAN, CLEAN with source coherence (CLEAN-SC), CLEAN-SC with compressed grids (CLEAN-SC-CG), CLEAN with cross spectral matrix function (CLEAN-CSM), High-resolution CLEAN-SC (HR-CLEAN-SC), are frequently mentioned and their advantages are widely discussed in the previous literatures. To assess their efficacy and suitability in engineering applications, with a focus on spatial resolution, computational efficiency, and dynamic range, a comparative study by locating two types of sound sources is carried out. The first scenario represents the case where the distance between two sound sources is smaller than the Rayleigh limit, while the second scenario represents the situation involving multiple sound sources, such as four or more complex sound sources. The analysis demonstrates that CBF, CLEAN, and CLEAN-SC cannot surpass the Rayleigh limit. However, FBF, CLEAN-SC-CG, CLEAN-CSM, and HR-CLEAN-SC have the potential to overcome it. In FBF, the grid mismatch results in a compromise between its dynamic range and source strength estimation. Meanwhile, HR-CLEAN-SC requires prior knowledge of the number of sound sources, which is challenging in applications. Because of superiority in the fundamental acoustic image and searching strategy, CLEAN-CSM and CLEAN-SC-CG exhibits superior features compared to the others. By compressing the number of grids, the CLEAN-SC-CG can improve the computational efficiency up to at least 46%. By constructing the cross spectral matrix function related to the real source, CLEAN-CSM uses the power function to simultaneously enhance the spatial resolution, dynamic range and source strength estimation. The conclusions are further validated through sound-source identification experiments involving two loudspeakers and an engine. The findings presented in this paper serve to guide the selection of suitable approaches for multi-sound source identification in engineering applications. [ABSTRACT FROM AUTHOR]

Accurate measurement is an important prerequisite for sound source localization. In the enclosed environments, noise and reverberation tend to cause localization errors. To address these issues, this paper proposes a compound model particle filter algorithm based on multi-feature. Based on a multi-feature observation, the likelihood function of speaker tracking is constructed for particle filter, and multi-hypothesis and frequency selection function are adopted to establish multi-feature optimization mechanism, including time delay selection and beam output energy fusion. It is found that they effectively solved the difficulty in the simultaneous suppression of noise and reverberation by single feature. Moreover, considering the randomness of speaker motion, a compound model for sound source tracking is developed, where the stability of the speaker tracking system is improved by integrating multi-feature observation into the compound model filtering. The experimental results with both simulated and real acoustic data indicate that the proposed method has better tracking performance, compared with the existing ones with low SNR and strong reverberation as well as highly mobile conditions. [ABSTRACT FROM AUTHOR]

The acoustic signals used by tool wear monitoring systems adopted in microdrilling are degraded by the noise present in manufacturing environments. To overcome this problem, the present study designed a system containing a 4 × 4 microelectromechanical system microphone array and a three-axis accelerometer to achieve accurate tool wear monitoring in noisy manufacturing environments. Features that were moderately to strongly correlated with tool wear were selected as inputs for this system's one-dimensional (1D) convolutional neural network (CNN) model for predicting tool wear. Pearson correlation analysis revealed that the frequency-domain signal features captured by a 4 × 4 microphone array with minimum variance distortion-less response (MVDR) beamforming had stronger correlations with tool wear than did those captured by a 4 × 4 or 1 × 2 microphone array with delay-and-sum beamforming or by a single microphone; this was because the signals captured by the 4 × 4 array with MVDR beamforming were less noisy. For the aforementioned microphone configurations, under the presence of noise, the 1D CNN model predicted severe wear with higher accuracy when it was trained using fused high-correlation features obtained from the signals captured by the 4 × 4 microphone array with MVDR beamforming and an accelerometer (97.6%) than when it was trained using unfused signal features captured by the accelerometer (86.7%) or the 4 × 4 microphone array with MVDR beamforming (90.3%) alone. The tool wear prediction accuracy obtained using the aforementioned fused features was close to that obtained using fused features acquired from the signals of one microphone and an accelerometer in a quiet environment (97.0%). [ABSTRACT FROM AUTHOR]

Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array. Using basis function decomposition, conventional SFR methods are straightforward and computationally efficient but may require more microphones than needed to measure the sound field. Recent studies show that pure data-driven and learning-based methods are promising in some SFR tasks, but they are usually computationally heavy and may fail to reconstruct a physically valid sound field. This paper proposes a compact acoustics-informed neural network (AINN) method for SFR, whereby the Helmholtz equation is exploited to regularize the neural network. As opposed to pure data-driven approaches that solely rely on measured sound pressures, the integration of the Helmholtz equation improves robustness of the neural network against variations during the measurement processes and prompts the generation of physically valid reconstructions. The AINN is designed to be compact and able to predict not only the sound pressures but also sound pressure gradients within a spatial region of interest based on measured sound pressures along the boundary. Experiments with acoustic transfer functions measured in different environments demonstrate the superiority of the AINN method over the traditional cylindrical harmonics and singular value decomposition methods. [ABSTRACT FROM AUTHOR]

For prompt detection of large (>1 kt) above-ground explosions, infrasound microphone networks and arrays are deployed at surveyed locations across the world. Denser regional and local networks are deployed for smaller explosions, however, they are limited in number and are often deployed temporarily for experiments. With the expanded interest in smaller yield explosions targeted at vulnerable areas such as population centers and key infrastructures, the need for more dense microphone networks has increased. An "attritable" (affordable, reusable, and replaceable) and flexible alternative can be provided by smartphone networks. Explosion signals from a fuel air explosive (thermobaric bomb) and a high explosive with trinitrotoluene equivalent yields of 6.35 and 3.63 kg, respectively, were captured on both an infrasound microphone and a network of smartphones. The resulting waveforms were compared in time, frequency, and time-frequency domains. The acoustic waveforms collected on smartphones produced a filtered explosion pulse due to the smartphone's diminishing frequency response at infrasound frequencies (<20 Hz) and was found difficult to be used with explosion characterization methods utilizing waveform features (peak overpressure, impulse, etc.). However, the similarities in time frequency representations and additional sensor inputs are promising for other explosion signal identification and analysis. As an example, a method utilizing the relative acoustic amplitudes for source localization using the smartphone sensor network is presented. [ABSTRACT FROM AUTHOR]

This paper presents the use of principal component analysis (PCA) for time domain microphone array denoising to characterize an impulsive aeroacoustic source, which is illustrated with the aeroacoustic emission caused by a vortex ring/edge interaction. Prior studies have used signal processing approaches that required assumptions about the source directivity or user intervention at low signal-to-noise ratio (SNR) conditions. In this context, PCA, a matrix decomposition tool which identifies the most common features across an ensemble of observations, provides a data-driven (hands-off) approach to signal processing. For microphone array time series, particular attention is paid to the temporal alignment of the signals to facilitate PCA. A time domain approach is necessary when sources are impulsive and nonstationary. Two such signal arrangements are discussed in this work. Results from this method are in good agreement with theory, validated by prior results using an ensemble averaging approach requiring user support. Furthermore, the results of this method are improved when compared to the ensemble averaging approach without user intervention. A SNR limit is identified where PCA becomes less effective for the vortex/edge interaction problem. This SNR limit is intended to aid in the design of similar future experiments. [ABSTRACT FROM AUTHOR]

The deconvolution DAMAS algorithm can effectively eliminate the misconceptions in the usually-used beamforming localization algorithm, allowing for a more accurate calculation of the source location as well as the intensity. When solving a linear system of equations, the DAMAS algorithm takes into account the mutual influence of different locations, reducing or even eliminating sidelobes and producing more accurate results. This work first introduces the principles of the DAMAS algorithm. Then it applies both the conventional beamforming algorithm and the DAMAS algorithm to simulate the localization of a single-frequency source from a 1.5 MW wind turbine, a complex line source with the text "UCAS" and a line source downstream of an airfoil trailing edge. Finally, the work presents experimental localization results of the source of a 1.5 MW wind turbine using both the conventional beamforming algorithm and the DAMAS algorithm. [ABSTRACT FROM AUTHOR]

This study introduces a new method for precise acoustic drone localization and tracking via the noise generated by the drone. Drone noises include harmonic frequency components which are related to the rotational speed of the propeller and the number of blades. This study integrates utilization of the frequency components around harmonics into Steered Response Power - Phase Transform (SRP-PHAT). First, a custom discrete Fourier transform (namely DFT-Harmonics) is defined which concentrates only on the vicinities of harmonics to capture the frequency components possibly related to the drone sound. Then, DFT-Harmonics is integrated into SRP-PHAT, which is named SRP-Harmonics. The benefits of SRP-Harmonics are explained and illustrated via SRP maps and videos. Experiments with real microphone array data show that SRP-Harmonics is precise in localizing and tracking a drone, while the ordinary SRP-PHAT can not be reasonably successful. Moreover, SRP-Harmonics after Kaiser window can maintain its performance even when significant level of artificial white noise or natural wind noise exists in the data. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a modified multiple signal classification (MUSIC) algorithm tailored for sound source identification (SSI) of vehicle noise is introduced and experimentally validated. A uniform planar microphone array (UPMA) is formulated for mathematical modeling, with its SSI-oriented parameters selected based on the primary frequency spectrum of vehicle noise. Simulations are conducted to compare the SSI accuracy of two conventional spatial spectra estimation (SSE) algorithms: the Capon algorithm and the MUSIC algorithm. The results demonstrate that the MUSIC algorithm, which relies on the eigenvalues of a covariance matrix to estimate signal direction, exhibits superior SSI resolution under low signal-to-noise ratio (SNR) conditions. However, it faces challenges in distinguishing between coherent or closely spaced signals. To address this, a modified MUSIC algorithm is proposed by reconstructing the covariance matrix of received signals and the SSE function. Simulation outcomes indicate that the modified MUSIC significantly outperforms the conventional version, owing to its enhanced SSI resolution. The accuracy of the SSI system, incorporating the UPMA and the modified MUSIC, is verified using a low-frequency volume source. Ultimately, the devised SSI system is successfully deployed to identify noise sources in a vehicle at different operational conditions, further validating the efficacy of the modified MUSIC. The UPMA and the modified MUSIC presented in this study have direct applicability in vehicle noise source identification and may be extended to other sound-related engineering fields for SSI purposes. [ABSTRACT FROM AUTHOR]

The primary cause of noise from vehicular traffic while travelling at speeds over 30 km/h is tyre/road interaction. To reduce this noise source, tyre/road sound emissions research has been carried out using different approaches. Most of this research has been centred around track tests, leading to the development of various track and road-based methods for evaluating tyre/road noise emissions. The CPX (Close-Proximity), along with the CPB (Controlled Pass-By), the CB (Coast-By) and the SPB (Statistical Pass-By), methods are the most common ones. Nevertheless, since Reg. (EC) 1222/2009 came into force, only the CB method, defined in Reg. (EC) 117/2007, can be used to obtain tyre/road noise emission type approval values in Europe. However, current track test methods have important limitations, such as the variability of the results depending on the test track or the test vehicle, the repeatability, the influence of environmental variables or, the main aspect, the limitation of the registered magnitude in these tests, which is the sound pressure level. The Alternative Drum test method (A-DR) was developed in 2015 in order to avoid these disadvantages. However, it involves a complex and time-consuming microphone array for each test. With the purpose of improving the A-DR test method, a new methodology based on drum tests, the ISO 11819-2 and the ISO 3744 standards, was developed. This paper describes the new Alternative CPX Drum test method (A-CPX-DR) and validates it by testing several tyres according to the CB, the A-DR and the A-CPX-DR test methods and comparing their results. This research has demonstrated that all three methods have equivalent sound spectra and obtain close equivalent sound pressure levels for type approval of tyres in the EU, while drum tests have shown greater accuracy. For both reasons, the new A-CPX-DR methodology could be used for tyre/road noise emission type approval in a more precise and cheaper way. [ABSTRACT FROM AUTHOR]

Traditional sound source localization (SSL) systems based on electret condenser microphone arrays are bulky because their localization accuracy depends on the size of the array. Inspired by the hearing mechanism of the parasitic fly Ormia ochracea, the localization accuracy of miniature bionic SSL devices breaks through the limitations of device size, but their ability to localize low-frequency sound sources over a wide angular range remains a challenge. In this work, a compact low-frequency SSL system with an extended directional range was prepared using two bionic micro-electro-mechanical system diaphragm based fiber-optic microphones, which form a non-coplanar array with a size of Φ44 mm × 13 mm. An algorithm for quantifying the azimuthal angle of a sound source is established for the prepared SSL system. Simulation and experimental results show that the prepared SSL system is capable of determining the propagation direction of acoustic signals with a frequency of less than 1 kHz in the azimuthal range from –90° to 90°, with a linear response in the range from −70° to 70°, and an angular measurement accuracy of the system within the range of ±7°. [ABSTRACT FROM AUTHOR]

The mouth of a speaker is generally close to the microphones of speech communication devices, such as headsets and phones, whereas noise sources are more distant from the microphones. Hence, near-field enhancement using microphone arrays is a promising strategy for noise suppression. This study deals with radiation mode–based microphone arrays and mainly focuses on experimentally validating the methodology. Because the radiation mode–based microphone array can be approximated as a gradient microphone, the difference between them and the advantage of the former over the latter are clarified by comparing the beampatterns. [ABSTRACT FROM AUTHOR]

The combination of neural networks and beamforming has proven very effective in multi-channel speech separation, but its performance faces a challenge in complex environments. In this paper, an iteratively refined multi-channel speech separation method is proposed to meet this challenge. The proposed method is composed of initial separation and iterative separation. In the initial separation, a time–frequency domain dual-path recurrent neural network (TFDPRNN), minimum variance distortionless response (MVDR) beamformer, and post-separation are cascaded to obtain the first additional input in the iterative separation process. In iterative separation, the MVDR beamformer and post-separation are iteratively used, where the output of the MVDR beamformer is used as an additional input to the post-separation network and the final output comes from the post-separation module. This iteration of the beamformer and post-separation is fully employed for promoting their optimization, which ultimately improves the overall performance. Experiments on the spatialized version of the WSJ0-2mix corpus showed that our proposed method achieved a signal-to-distortion ratio (SDR) improvement of 24.17 dB, which was significantly better than the current popular methods. In addition, the method also achieved an SDR of 20.2 dB on joint separation and dereverberation tasks. These results indicate our method's effectiveness and significance in the multi-channel speech separation field. [ABSTRACT FROM AUTHOR]

Speaker diarization consists of answering the question of "who spoke when" in audio recordings. In meeting scenarios, the task of labeling audio with the corresponding speaker identities can be further assisted by the exploitation of spatial features. This work proposes a framework designed to assess the effectiveness of combining speaker embeddings with Time Difference of Arrival (TDOA) values from available microphone sensor arrays in meetings. We extract speaker embeddings using two popular and robust pre-trained models, ECAPA-TDNN and X-vectors, and calculate the TDOA values via the Generalized Cross-Correlation (GCC) method with Phase Transform (PHAT) weighting. Although ECAPA-TDNN outperforms the Xvectors model, we utilize both speaker embedding models to explore the potential of employing a computationally lighter model when spatial information is exploited. Various techniques for combining the spatial–temporal information are examined in order to determine the best clustering method. The proposed framework is evaluated on two multichannel datasets: the AVLab Speaker Localization dataset and a multichannel dataset (SpeaD-M3C) enriched in the context of the present work with supplementary information from smartphone recordings. Our results strongly indicate that the integration of spatial information can significantly improve the performance of state-of-the-art deep learning diarization models, presenting a 2–3% reduction in DER compared to the baseline approach on the evaluated datasets. [ABSTRACT FROM AUTHOR]

In order tomake detailed and accurate predictions of aircraft noise around airports, we have developed "J-FRAIN", a new aircraft noise simulation framework that can precisely predict the time histories of noises emitted from each major aircraft noise source during the landing approach phase at ground observation points. This article describes the elements of the developed framework--data acquisition, sound source modeling, propagation modeling, and ground noise prediction--and present some application examples. To develop the framework, we first deployed a 30m-diameter microphone array under the final approach path to an international airport to measure acoustic maps of several civil aircraft types in flight. The sound powers of major aircraft noise-emitting componentswere then estimated quantitatively by domain integration of the deconvolved acoustic maps at five emission angles in the plane of the glideslope, and the directivities of each noise sourcewere determined. Next, componentwise sound source regression models for engines and airframe noise sourceswere created based on the physical relationships between engine rotation speed, airspeed, and the deployment angle of high-lift devices, and the coefficients in each modelwere determined to minimize the root-mean-square error between themeasured and predicted sound power levels. The phenomena of atmospheric absorption and ground effect during the propagation of radiated sounds were also incorporated into the framework. Actual flight parameter values were used as inputs to the completed framework, and it was confirmed that the predicted time histories of sound pressure levels on the ground agreed with measured data towithin 2 dB if ground effect was properly considered. Finally, as sample applications that use the unique features of the proposed J-FRAIN framework, the article discusses the evaluation of the contributions of each aircraft noise source at noise observation points under the final approach path to the airport and the impact assessment of flight operations on noise. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Science is the property of Alcide De Gasperi University of Euroregional Economy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work introduces a large dataset comprising impulse responses of spatially distributed sources within a plane parallel to a planar microphone array. The dataset, named MIRACLE, encompasses 856,128 single-channel impulse responses and includes four different measurement scenarios. Three measurement scenarios were conducted under anechoic conditions. The fourth scenario includes an additional specular reflection from a reflective panel. The source positions were obtained by uniformly discretizing a rectangular source plane parallel to the microphone for each scenario. The dataset contains three scenarios with a spatial resolution of 23 mm at two different source-plane-to-array distances, as well as a scenario with a resolution of 5 mm for the shorter distance. In contrast to existing room impulse response datasets, the accuracy of the provided source location labels is assessed and additional metadata, such as the directivity of the loudspeaker used for excitation, is provided. The MIRACLE dataset can be used as a benchmark for data-driven modelling and interpolation methods as well as for various acoustic machine learning tasks, such as source separation, localization, and characterization. Two timely applications of the dataset are presented in this work: the generation of microphone array data for data-driven source localization and characterization tasks and data-driven model order reduction. [ABSTRACT FROM AUTHOR]

This paper is concerned with the estimation of the direction and distance of sound sources with the MUSIC beamforming algorithm, and their tracking with the help of Kalman filter. Direction-of-arrival (DOA) estimations can be performed using a combination of acoustical focusing and beamforming. Distance estimation is usually not part of the process, but it is possible through an extension of the beamforming algorithm. MUSIC (Multiple Signal Classification) is a relatively fast and simple method to locate sound sources. It is based on the separation of the received signals’ cross-spectral matrix to signal and noise subspaces. We also use the Kalman filter and its extended non-linear version to track moving sound sources. We evaluate the performance of these methods through simulations in the MATLAB environment and measurements with unmanned aerial vehicles (UAV). DOA estimations and tracking are possible in both cases, but distance estimation is significantly more problematic in the latter. We aim to find the cause of the errors in the estimation during measurements, to develop a more robust method in the future. [ABSTRACT FROM AUTHOR]

For noise reduction at a low-to-moderate Reynolds number, airfoil trailing-edge tonal noise has multiple prominent tones. Among these tones, secondary tones are greatly influenced by external disturbances such as oscillations commonly in the environment. In previous experiments, the spatial movement of sources was found to be related to an inherent high-frequency oscillation. Therefore, the spatial influence of external low-frequency oscillations was investigated in this study. By using tripping tapes to construct different symmetry source regions on the pressure side with side secondary tones, a transient spatial analysis of an NACA0012 airfoil at 2 degrees was performed by microphone arrays when a 10 Hz pressure oscillation was significant at 24 m/s. Temporally, this 10 Hz periodic strength change became more intense at a broader frequency bandwidth for a longer source region. Furthermore, a substantial time delay, significantly larger than the sound propagating time difference between microphones, was observed exclusively along the spanwise direction. This delay led to a periodic directivity pattern, particularly when two 0.2 m source regions were separated by a 0.2 m or 0.4 m tripping region. This low-frequency oscillation introduces an asymmetric transient switching pattern for symmetric spanwise source regions. Consequently, the response of airfoils to external oscillations in field tests should be considered. [ABSTRACT FROM AUTHOR]

This study proposes a framework that reduces the calculation cost of sound source localization with the Amiet model, using a data-driven sparse sampling method. This method accelerates the calculation of the steering vector used in conventional beamforming. An aeroacoustic wind tunnel test was conducted in a 2 × 2 m2 low-speed wind tunnel, and the proposed method was verified. During the test, a monopole laser sound source, which does not interfere with the flow, was used, and its acoustic signals were measured using a microphone array. Next, steering vectors were reconstructed by discovering dominant modes and optimized sampling points from the training data based on the Amiet model and the modified data-driven sparse sampling method. Finally, the sound-source positions when the steering vector of the proposed model was used were compared with the positions observed when the steering vector of which all the grid points were calculated was used. The error was less than 2 mm when 16 modes were used, and the calculation time was reduced to ∼1/33 of that of the previous Amiet model. [ABSTRACT FROM AUTHOR]

Recent advancements in transfer learning have revolutionized predictive maintenance, enabling cross-domain generalization for components with varying characteristics and operating under different conditions. While traditional transfer learning approaches require labeled data in both source and target domains, unsupervised transfer learning strives for a more cost-effective alternative for which only labels are available in the source domain. This study investigates adversarial transfer learning between two different sensor modalities: vibration and acoustic. The goal is to enable bearing monitoring using microphones, which are, in general terms, cheaper and easier to deploy than vibration sensors; and without the need to label data in the target domain. The research goal is to identify the operating speed of a bearing testbed. The source domain data correspond to vibration measurements taken from an attached sensor, while the target domain uses a microphone array at distance. Artificial Neural Networks are used as the base architecture. Transferability is assessed with two unsupervised adversarial learning techniques: gradient reversal and deep correlation alignment. Their performance is compared to traditional supervised transfer learning via fine-tuning. Experimental results demonstrate that gradient reversal outperforms deep correlation alignment and is able to achieve results similar to those obtained with supervised transfer learning. These findings highlight the feasibility of speed identification using a microphone array and establish a baseline for future condition monitoring research with such sensors. [ABSTRACT FROM AUTHOR]

In recent years, drones have brought about numerous conveniences in our work and daily lives due to their advantages of low cost and ease of use. However, they have also introduced significant hidden threats to public safety and personal privacy. Effectively and promptly detecting drone is thus a crucial task to ensure public safety and protect individual privacy. This paper proposes a method that combines beamforming algorithm with Deep Learning neural network to achieve the detection of drone acoustic event using microphone array technology. The aim is to achieve maximum coverage and accuracy in drone detection. The proposed approach utilizes beamforming algorithm to perform directional audio capture of the drone sound signal acquired by the microphone array. It then extracts features such as Log-Mel spectrogram and Mel-Frequency Cepstral Coefficients from the audio signal, which are subsequently input to a Convolutional Neural Network for classification. The final detection result is obtained through this process. The study also incorporates experimental analysis to assess the impact of different frontend processing algorithms, dataset compositions and feature selections on the detection performance. To provide a more specific and pronounced indication of the accomplishment of the drone sound event detection task, a novel evaluation criterion is introduced, termed as the Machine- Human Ultimate Distance Ratio. This criterion is employed to assess the detection effectiveness of the drone sound event detection task. The results demonstrate that the detection range and accuracy of the drone sound event detection system based on Deep Learning and microphone array surpass those of single-microphone sound event detection method. The proposed detection approach achieves effective detection within a range of up to 135 m in the surrounding environment. [ABSTRACT FROM AUTHOR]

InsectSound1000 is a dataset comprising more than 169000 labelled sound samples of 12 insects. The insect sound level spans from very loud (Bombus terrestris) to inaudible to human ears (Aphidoletes aphidimyza). The samples were extracted from more than 1000 h of recordings made in an anechoic box with a four-channel low-noise measurement microphone array. Each sample is a four-channel wave-file of 2500 kHz length, at 16 kHz sample rate and 32 bit resolution. Acoustic insect recognition holds great potential to form the basis of a digital insect sensor. Such sensors are desperately needed to automate pest monitoring and ecological monitoring. With its significant size and high-quality recordings, InsectSound1000 can be used to train data-hungry deep learning models. Used to pretrain models, it can also be leveraged to enable the development of acoustic insect recognition systems on different hardware or for different insects. Further, the methodology employed to create the dataset is presented in detail to allow for the extension of the published dataset. [ABSTRACT FROM AUTHOR]

The longitudinal tear of conveyor belts is the most common accident occurring at the workplace. Given the limitations on accuracy and stability of current single-modal approaches to detecting the longitudinal tear of conveyor belts, a solution is proposed in this paper through Audio-Visual Fusion. According to this method, a linear CCD camera is used to capture the images of the conveyor belt and a microphone array for the acquisition of sound signals from the operating belt conveyor. Then, the visual data is inputted into an improved Shufflenet_V2 network for classification, while the preprocessed sound signals are subjected to feature extraction and classification using a CNN-LSTM network. Finally, decision fusion is performed in line with Dempster-Shafer theory for image and sound classification. Experimental results show that the method proposed in this paper achieves an accuracy of 97% in tear detection, which is 1.2% and 2.8% higher compared to using images or sound alone, respectively. Apparently, the method proposed in this paper is effective in enhancing the performance of the existing detection methods. [ABSTRACT FROM AUTHOR]

To better combat the devastation of the protected forests in Côte d'Ivoire, a study was conducted to create a technique for detecting the acoustic signals produced by chainsaws deployed to fell trees in these areas. To improve the recognition rate of chainsaw sounds in a forest environment and increase the detection range of the recognition system, we are implementing the maximum ratio combining (MRC) technique on a microphone array. Therefore, the employment of an identification system is compared using one (01) microphone against the outcomes obtained by adopting system with three (03), six (06), and twelve (12) microphones. The use of MRC is then contrasted with an alternative recombining approach, designated as simple summation (SS). The SS is characterised by the simple addition of the signals acquired by the microphone array in the frequency domain. The MRC was employed on various microphone arrangements, accounting for varying degrees of attenuation experienced by chainsaw sounds. The K-Nearest Neighbors, in combination with Mel Frequency Cepstral Coefficients (MFCC), was employed to detect chainsaw sounds within the 16 kHz central frequency octave band. MRC applied to microphone arrays provided superior outcomes than simple summation. The enhancement in terms of classification rate ranged from [18; 51], favouring MRC. Moreover, it extended the chainsaw detection range from 520 m (using one microphone) to 1210 m (using a 12-microphone array). Taking into account the criteria for selecting an optimum microphone array, including classification rate, number of microphone nodes, information processing time and detection range, the six-microphone array was chosen as the best configuration. This configuration boasts a theoretical detection range of 1040 meters. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker's direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot's surroundings. [ABSTRACT FROM AUTHOR]

We propose a novel high‐performance dual‐resonance enhanced photoacoustic spectroscopy (DRE‐PAS) gas sensor based on a highly sensitive fiber optic cantilever beam microphone and a high‐Q spherical photoacoustic cell (PAC). The first‐order resonant frequency (FORF) of the spherical PAC is analyzed by finite element analysis to match the FORF of the cantilever microphone for the double resonance enhancement of the photoacoustic signal. The photoacoustic spectroscopy (PAS) system, including the DRE‐PAS sensor, a 1532.8 nm distributed feedback laser, and a high‐speed spectrometer, has been successfully exploited for trace acetylene (C2H2) detection. The experimental results show that the limit of detection (LOD) is 106.8 parts‐per‐billion (ppb) with an integral time of 1 s, and the LOD can be further reduced to 11.03 ppb by Allan‐Werle deviation for 100 s integral time. The normalized noise equivalent absorption coefficient can be obtained as 2.44 × 10−8 cm−1 WHz−1/2. The reported DRE‐PAS gas sensor has the superior characteristics of photoacoustic signal enhancement, high sensitivity, and strong antielectromagnetic interference capability, which can provide a new solution for PAS development. [ABSTRACT FROM AUTHOR]

Speech improvement is an indispensable technology in the field of speech interaction. Speech enhancement uses a variety of techniques and algorithms to increase the quality and intelligibility of speech. Background noise is always present when there is a speech signal. To retrieve the intended speech signal from the damaged speech signal, speech systems must use excellent noise reduction algorithms. This article presents an application-oriented approach for separating and enhancing preferred speech signals using circular microphone array for audio conferencing systems. The source separation for conferencing (SSC)method calculates the number of active speech signals and the direction of arrival by processing the arrival angle, phase variations, and the time difference in arrival. The direction of the signal is determined using Time Difference of Arrival (TDOA) technique. The adaptive least mean square (LMS) algorithm and the transformed TDOA signal can be enhanced to obtain the preferred signal from the selected location. The SSC method yields an SNR of 7.8 dB, SDR of 6.9 dB, SIR of 8.3 dB, and PESQ of 2.2 as the signal quality metrics of an input signal to the enhanced desired signal. The efficiency of the proposedmodel is compared with the existingmodels like PL-CNN, DL-MVDR, and SUDoRMRF. [ABSTRACT FROM AUTHOR]

This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference. [ABSTRACT FROM AUTHOR]

The increasing prevalence of smart devices spurs the development of emerging indoor localization technologies for supporting diverse personalized applications at home. Given marked drawbacks of popular chirp signal-based approaches, we aim at developing a novel device-free localization system via the continuous wave of the inaudible frequency. To achieve this goal, solutions are developed for fine-grained analyses, able to precisely locate moving human traces in the room-scale environment. In particular, a smart speaker is controlled to emit continuous waves at inaudible 20kHz, with a co-located microphone array to record their Doppler reflections for localization. We first develop solutions to remove potential noises and then propose a novel idea by slicing signals into a set of narrowband signals, each of which is likely to include at most one body segment's reflection. Different from previous studies, which take original signals themselves as the baseband, our solutions employ the Doppler frequency of a narrowband signal to estimate the velocity first and apply it to get the accurate baseband frequency, which permits a precise phase measurement after I-Q (i.e., in-phase and quadrature) decomposition. A signal model is then developed, able to formulate the phase with body segment's velocity, range, and angle. We next develop novel solutions to estimate the motion state in each narrowband signal, cluster the motion states for different body segments corresponding to the same person, and locate the moving traces while mitigating multi-path effects. Our system is implemented with commodity devices in room environments for performance evaluation. The experimental results exhibit that our system can conduct effective localization for up to three persons in a room, with the average errors of 7.49cm for a single person, with 24.06cm for two persons, with 51.15cm for three persons. [ABSTRACT FROM AUTHOR]

In addition to the filter coefficients, the location of the microphone array is a crucial factor in improving the overall performance of a beamformer. The optimal microphone array placement can considerably enhance speech quality. However, the optimization problem with microphone configuration variables is non-convex and highly non-linear. Heuristic algorithms that are frequently employed take a long time and have a chance of missing the optimal microphone array placement design. We extend the Bayesian optimization method to solve the microphone array configuration design problem. The proposed Bayesian optimization method does not depend on gradient and Hessian approximations and makes use of all the information available from prior evaluations. Furthermore, Gaussian process regression and acquisition functions make up the Bayesian optimization method. The objective function is given a prior probabilistic model through Gaussian process regression, which exploits this model while integrating out uncertainty. The acquisition function is adopted to decide the next placement point based upon the incumbent optimum with the posterior distribution. Numerical experiments have demonstrated that the Bayesian optimization method could find a similar or better microphone array placement compared with the hybrid descent method and computational time is significantly reduced. Our proposed method is at least four times faster than the hybrid descent method to find the optimal microphone array configuration from the numerical results. [ABSTRACT FROM AUTHOR]

A time-domain approach for interior spherical near-field acoustic holography is proposed to achieve the low-delay reconstruction of time-domain sound fields using a rigid spherical microphone array. This reconstruction encompasses the incident pressure field, the incident radial particle velocity field, and the total pressure field, which includes scattering. The proposed approach derives time-domain radial propagators through the inverse Fourier transform of their frequency-domain counterparts. These propagators are then applied to the array measurements to obtain the time-domain spherical harmonic coefficients of the interior sound field. Given the fact that the time-domain radial propagators possess finite-time support and exhibit significant high-frequency attenuation characteristics, they can be efficiently implemented using finite impulse response (FIR) filters. The proposed approach processes the signal sample-by-sample through these FIR filters, avoiding a series of issues associated with time-frequency transformations in frequency-domain methods. As a result, the approach offers higher accuracy and lower latency in reconstructing non-stationary sound fields compared to its frequency-domain counterpart and thus holds greater potential for real-time applications. Additionally, owing to the scattering effect of the rigid sphere, the approach avoids the impact of spherical Bessel function nulls and does not require the measurement of particle velocities, which renders the measurements cost effective. [ABSTRACT FROM AUTHOR]

Microphone array-based beamforming algorithms are widely used in sound source identification, fault diagnosis, and radar communication because of their excellent performance. However, their limited spatial resolution and high dynamic side flap level seriously affect the recognition accuracy. To explore a high-performance beamforming sound source identification algorithm, the microphone array compressed beamforming underdetermined equation is solved by extending the iterative threshold. A sound source identification model is established, and a new compressed beamforming (CSB-II) algorithm is proposed. Numerical simulations show that the CSB-II algorithm can effectively reduce the starting frequency of sound source identification and has high sound source identification accuracy. The effects of signal-to-noise ratio, sound source distance, and array number on sound source identification accuracy are analyzed separately. The laws affecting sound source identification accuracy are derived from guiding actual sound source measurements. [ABSTRACT FROM AUTHOR]

Accurate direction of arrival (DoA) estimation is paramount in various fields, from surveillance and security to spatial audio processing. This work introduces an innovative approach that refines the DoA estimation process and demonstrates its applicability in diverse and critical domains. We propose a two-stage method that capitalizes on the often-overlooked secondary peaks of the cross-correlation function by introducing a reduced complexity DoA estimation method. In the first stage, a low complexity cost function based on the zero cyclic sum (ZCS) condition is used to allow for an exhaustive search of all combinations of time delays between pairs of microphones, including primary peak and secondary peaks of each cross-correlation. For the second stage, only a subset of the time delay combinations with the lowest ZCS cost function need to be tested using a least-squares (LS) solution, which requires more computational effort. To showcase the versatility and effectiveness of our method, we apply it to the challenging acoustic-based drone DoA estimation scenario using an array of four microphones. Through rigorous experimentation with simulated and actual data, our research underscores the potential of our proposed DoA estimation method as an alternative for handling complex acoustic scenarios. The ZCS method demonstrates an accuracy of 89.4 % ± 2.7 % , whereas the ZCS with the LS method exhibits a notably higher accuracy of 94.0 % ± 3.1 % , showcasing the superior performance of the latter. [ABSTRACT FROM AUTHOR]

Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source. [ABSTRACT FROM AUTHOR]

Sound source localization technology has gradually become one of the main methods for fault source target localization in dangerous and difficult-to-discover applications. In order to locate the target fault sound source more intuitively, accurately and in real time in practical applications, an audio-visual positioning system is developed. A 64-channel microphone array is designed, which is evenly distributed in a circular shape, and has a video acquisition target at the same time. The system uses GCC-PHAT to estimate the relative time delay between the signal source arriving at two microphones, and then uses SRP-PHAT algorithm to obtain weighted sum beamforming. A spatial grid search strategy based on spherical coordinate spatial contraction method is proposed. When the output power is maximum, the direction corresponding to the beam is regarded as the sound source direction. Using the same sound source signal, the proposed positioning method is compared with the direct Newton iterative method and the Cartesian coordinate scanning SRP-based maximum method. The simulation results show that the Cartesian coordinate value and spherical coordinate value of the positioning algorithm in this article are closer to the real coordinates of the sound source position than the other two methods, and the positioning operation time is the shortest. In addition, in the actual corona discharge power source positioning experiment, the system can accurately locate the insulator pollution discharge point, which is consistent with the infrared detection and positioning results and has good robustness. Therefore, the system location method can provide a new choice for sound source location applications. [ABSTRACT FROM AUTHOR]

The rapid growth of drone use in urban areas has prompted authorities to review airspace regulations, forcing drone manufacturers to anticipate and reduce the noise emissions during the design stage. Additionally, micro air vehicles (MAVs) are designed to be aerodynamically efficient, allowing them to fly farther, longer and safer. In this study, a steady aerodynamic code and an acoustic propagator based on the non-linear vortex lattice method (NVLM) and Farassat's formulation-1A of the Ffowcs Williams and Hawkings (FW-H) acoustic analogy, respectively, are coupled with pymoo, a python-based optimization framework. This tool is used to perform a multi-objective (noise and aerodynamic efficiency) optimization of a 20 cm diameter two-bladed rotor under hovering conditions. From the set of optimized results, (i.e., the Pareto front), three different rotors are 3D-printed using a stereolithography (SLA) technique and tested in an anechoic room. Here, an array of far-field microphones captures the acoustic radiation and directivity of the rotor, while a balance measures the aerodynamic performance. Both the aerodynamic and aeroacoustic performance of the three different rotors, in line with what has been predicted by the numerical codes, are compared and guidelines for the design of aerodynamically and aeroacoustically efficient MAV rotors are extracted. [ABSTRACT FROM AUTHOR]

When rotating noise sources, such as turbomachinery, are investigated using phased microphone array measurements and beamforming, sidelobes appear on the resulting beamforming maps. Sidelobes can be decreased by increasing the number of microphones. However, if the investigated phenomenon is steady, then there is a cost-effective alternative: performing asynchronous measurements using phased arrays having a limited number of microphones. The single beamforming maps can be combined in order to arrive at results that are superior in resolution and sidelobe levels. This technique has been investigated in the literature, but according to the authors' best knowledge, has not yet been applied to turbomachinery. This article introduces a means for applying the asynchronous measurement technique and the combination methods for rotating noise sources. The combination methods are demonstrated on two rotating point sources (both in simulations and measurements), and then on an axial flow fan test case. In the case of the two rotating point sources, the achievable improvement in resolution, average-, and maximum sidelobe levels are shown as compared to the single results. In the case of the axial flow fan, it is demonstrated that the combination methods provide more reliable noise source locations and reveal further noise sources. [ABSTRACT FROM AUTHOR]

In this work, an algorithm that combines deep-learning based dereverberation and beamforming is proposed for sound source localization in the reverberant environment. The contribution is combining deep-learning based dereverberation and beamforming together. Through deep learning, the proposed algorithm can directly achieve dereverberation of the cross-spectral matrix of microphone array measurement signals in the reverberant environment, which can overcome the challenge of requiring multiple measurements like average beamforming. In this way, the proposed algorithm can be applied to the reverberant environment that requires efficient sound source localization. At the same time, the network of deep-learning based dereverberation is trained directly using the cross-spectral matrix of signals collected by microphone arrays, which can overcome the challenge of requiring prior knowledge of reflective surfaces like empirical dereverberation beamforming. In this way, the proposed algorithm can be applied to the reverberant environment composed of walls with unknown sound reflection coefficients. Both specular reflections, diffuse reflections, and background noise are considered in the reverberant environment. The cross-spectral matrix of microphone array measurement signals in the reverberant environment is first fed into the U-net for dereverberation and then combined with beamforming for sound source localization. The results in the test set show that the proposed algorithm can eliminate the influence of reverberation on sound source localization. The applicability limitation results of the proposed algorithm show the proposed algorithm's strong robustness to unseen sound reflection coefficient, unseen SNR, and expandability to some degree for unseen source location. However, the proposed algorithm cannot provide stable and accurate sound source localization results under unseen room geometry. The proposed algorithm has higher spatial resolution and smaller errors than the average beamforming and the empirical dereverberation beamforming. [ABSTRACT FROM AUTHOR]