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To address the limitations of conventional linear array beamformers, such as their single functionality and poor signal-to-noise ratio ( SNR) gain, a controllable beamforming method for rectangular arrays was proposed. The method was based on the Kronecker product principle, where a rectangular array was decomposed into two virtual linear sub-arrays for analysis. The steering vector of the rectangular array was represented as the Kronecker product of the steering vectors of the two virtual sub-arrays. A differential virtual sub-array with controllable nulls was then designed by utilizing the orthogonality between the beamforming filter and the steering vector. The second virtual sub-array was designed using a delay-sum beamformer (DS) and a superdirective beamformer (SD). The two virtual sub-arrays were then integrated to create a high-gain, null-controllable beamformer and a high-directivity, null-controllable beamformer based on a rectangular array. Simulation experiments analyzed the beam patterns, white noise gain (WNG), and directivity factor (DF) of the rectangular array and the two virtual linear sub-arrays. The results show that the proposed beamformer achieves controllable nulls and significantly improves the SNR gain compared to a single virtual sub-array. It is suggested that this approach be applied in scenarios where high-gain and high-directivity beamforming are required. [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]

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]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Time reverse modeling (TRM) is successfully applied to acoustic signals from a circular microphone array, for mapping of sudden cracking sound events. Numerical feasibility using synthetic acoustic sources followed by an experimental study with steel pendulum impacts on a steel plate is carried out. The mapping results from the numerical and experimental data are compared and verified using a delay-and-sum beamforming technique. Based on the feasibility and experimental study, a mapping error is estimated. In the main experimental study, cracking sound events obtained during a tensile test on a textile-reinforced concrete specimen are mapped with the TRM. The enhanced capability of the TRM to map simultaneously occurring cracking sound events along crack paths is demonstrated. [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]

Purpose: The accuracy of real-time sound source localization is an important issue in acoustics. The steered sample algorithm (SSA), an algorithm developed based on the reciprocity of wave propagation, has a higher spatial resolution than the steered response power – phase transform (SRP-PHAT) algorithm. The algorithm can also render an accurate sound source localization under limited array elements and low signal-to-noise ratio. However, the actual implementation of the algorithm is usually based on an expensive grid search process, which makes the computational cost a serious problem. Methods: An improved implementing algorithm based on beetle swarm optimization (BSO) is proposed for SSA, which can effectively reduce the computational cost. The modified algorithm has a good convergence speed as the SSA algorithm has fewer local extremums. Results: Experimental results demonstrate that compared with steered sample algorithm (SSA), the proposed algorithm has almost the same localization performance and robustness with lower computational cost. Conclusion: This paper proposes an improved algorithm about SSA algorithm. Compared with SRC algorithm, the amount of computation is reduced more than twice. Meanwhile, the proposed algorithm inherits the anti-noise performance of SSA. Under the condition of low SNR, the positioning success rate and RMSE performance are excellent. Under the condition of high reverberation, the improved algorithm needs more particles to ensure the positioning performance. When the number of particles is not less than 70, the localization success rate of the proposed algorithm is highly consistent with the conventional SSA, and the RMSE is slightly less than the conventional SSA. Compared with SRC algorithm, the proposed algorithm better inherits the robustness of original SSA and improves the positioning accuracy. [ABSTRACT FROM AUTHOR]

A set of microphones spatially arranged in space in a specific pattern is called a microphone array. It can be used to extract and enhance the signal of interest from its observation corrupted by other interfering signals, such as noise or to estimate the direction of arrival of a source. In this paper we focus on a problem in which the desired signal (speech signal) is interfered by other signal with fully overlapping bandwidth but with different localization. Our goal is to attenuate the interfering signal. We experimentally study the method in which microphones do not have to be equally spaced and all information regarding signal phase is hidden in a transfer function of the microphone. We focus on determining the microphones positions and FIR filter coefficients so that the actual output the beamformer is as close as possible to the desired one (the level of speech signal remains unchanged, while the interfering signal is attenuated) in the sense of ���� norm. To solve this problem, we use a metaheuristic algorithm. Next, we construct the array and make an experiment in anechoic chamber. The initial results of the experiment show that the proposed method can be applied for array designing.

Abstract 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 $$23\,\textrm{mm}$$ 23 mm at two different source-plane-to-array distances, as well as a scenario with a resolution of $$5\,\textrm{mm}$$ 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.

Acoustic cameras (ACs) have become very popular in the last decade as an increasing number of applications in environmental acoustics are observed, which are mainly used to display the points of greatest noise emission of one or more sound sources. The results obtained are not yet certifiable because the beamforming algorithms or hardware behave differently under different measurement conditions, but at present, not enough studies have been dedicated to clarify the issues. The present study aims to provide a methodology to extract analytical features from sound maps obtained with ACs, which are generally only visual information. Based on the inputs obtained through a specific measurement campaign carried out with an AC and a known sound source in free field conditions, the present work elaborated a methodology for gathering the coordinates of the maximum emission point on screen, its distance from the real position of the source and the uncertainty associated with this position. The results obtained with the proposed method can be compared, thus acting as a basis for future comparison studies among calculations made with different beamforming algorithms or data gathered with different ACs in all real case scenarios. The method can be applicable to any other sector interested in gathering data from intensity maps not related to sound. [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]

A set of microphones spatially arranged in space in a specific pattern is called a microphone array. It can be used to extract and enhance the signal of interest from its observation corrupted by other interfering signals, such as noise or to estimate the direction of arrival of a source. In this paper we focus on a problem in which the desired signal (speech signal) is interfered by other signal with fully overlapping bandwidth but with different localization. Our goal is to attenuate the interfering signal. We experimentally study the method in which microphones do not have to be equally spaced and all information regarding signal phase is hidden in a transfer function of the microphone. We focus on determining the microphones positions and FIR filter coefficients so that the actual output the beamformer is as close as possible to the desired one (the level of speech signal remains unchanged, while the interfering signal is attenuated) in the sense of l2 norm. To solve this problem, we use a metaheuristic algorithm. Next, we construct the array and make an experiment in anechoic chamber. The initial results of the experiment show that the proposed method can be applied for array designing. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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]

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]

Realization of an efficient system for extracting useful audio signals in the presence of interferences is an important engineering problem in the field of acoustics. This paper proposes a method for extracting sound signals from a specific direction of incidence. A microphone array consisting of eight microphones was used, coupled with space-time signal processing executed in real time on hardware. The microphone signals are filtered by an adaptive beamformer, whose coefficients are optimized in real time on open-source hardware using the Least Mean Square algorithm. This paper describes the characteristics and limitations of the used microphone array. Testing the system through simulation indicates that extracting narrowband and wideband signals using the presented adaptive beamforming method is theoretically possible. This paper aims to experimentally evaluate the algorithm under real-world conditions, as well as identify the limitations of extracting wideband signals. Adaptation of the filter coefficients and filtering the signals from the microphones was implemented on Bela hardware, specialized for processing audio signals. This experiment highlights the limitations that arise from extracting a wideband signal using this type of hardware in real conditions. By comparing the useful and filtered signals in both time and frequency domains, the quality of the filtering was analyzed. Our experiments suggest that the current hardware specifications are a limiting factor for successful wideband signal filtering in real conditions, as they limit the maximum possible order of the adaptive filter, which proved to be insufficient. [ABSTRACT FROM AUTHOR]

This paper conducts in‐depth research on the sound source localization technology of the microphone array and designs a sound source localization system based on the cube microphone array. Firstly, the sound source localization model is established using the cube microphone array combined with the spherical near‐field acoustic holography. Secondly, the numerical and sound source localization simulations are carried out using the spherical wave. Finally, the simulation and experiment of sound source location for sound at 100, 1000, and 2000 Hz are carried out using the model. Both simulation and experimental results show that when the sound source frequency is 100 and 1000 Hz, the location of the sound source can be accurately located by using the sound source localization model of a cube microphone array, and the sound field reconstruction error is low. When the sound source frequency is 2000 Hz, the location of the sound source cannot be located, and the sound field reconstruction error is very high, which will cause the misjudgment of the sound source location. [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]

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]

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 use of machine learning for localization of sound sources from microphone array data has increased rapidly in recent years. Newly developed methods are of great value for hearing aids, speech technologies, smart home systems or engineering acoustics. The existence of openly available data is crucial for the comparability and development of new data-driven methods. However, the literature review reveals a lack of openly available datasets, especially for large microphone arrays. This contribution introduces a framework for generation of acoustic data for machine learning. It implements tools for the reproducible random sampling of virtual measurement scenarios. The framework allows computations on multiple machines, which significantly speeds up the process of data generation. Using the framework, an example of a development dataset for sound source characterization with a 64-channel array is given. A containerized environment running the simulation source code is openly available. The presented approach enables the user to calculate large datasets, to store only the features necessary for training, and to share the source code which is needed to reproduce datasets instead of sharing the data itself. This avoids the problem of distributing large datasets and enables reproducible research. [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]

The intrinsic noise of different transformers in the same substation belongs to the same type of noise, which is strongly coherent and difficult to separate, greatly increasing the cost of substation noise assessment and treatment. To solve the problem, the present paper proposes a noise separation technique using the phase conjugation method to separate the intrinsic noise signals of different transformers: firstly, the reconstruction of sound source information is realized by the phase conjugation method based on the measurement and emission of a line array; secondly, the intrinsic noise signals of the sound source are obtained by the equivalent point source method. The error of the separation technique is analyzed by point source simulation, and the optimal arrangement form of the microphone line array is studied. A validation experiment in a semi-anechoic chamber is also carried out, and the results prove that the error of separation technique is less than 2dBA, which is the error tolerance of engineering applications. Finally, a noise separation test of three transformers is performed in a substation using the proposed technique. The results show that the proposed technique is able to realize the intrinsic noise separation of each transformer in the substation, which is of positive significance for substation noise assessment and management. [ABSTRACT FROM AUTHOR]

The auditory system is considered one of the crucial pathways for robots to perceive environmental information. The perception and decision-making capabilities of mobile robots are greatly enhanced by accurate and effective sound source localization, making it highly significant for applications in hazardous environment rescue and inspection. With the widespread adoption of deep learning, the effectiveness of sound source localization has been notably improved through the introduction of convolutional neural networks (CNNs). Sound source localization for mobile robots was comprehensively compared and analyzed from four perspectives: network architecture and improvements, types of sound features, data simulation and augmentation, as well as the fusion of multimodal information. Reflections and prospects on the application of the technology are also presented. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced approach for sound localization is proposed, which fuses automatic extraction of array signal characteristic frequencies and adaptive weighting. The method refines the autoregressive power spectral estimation algorithm and improves density-based spatial clustering of applications with noise algorithm for characteristic frequency extraction. Adaptive weighting technique is introduced to alleviate the problem of frequency mismatch in the localization process. The initial weight of narrowband signals is calculated and normalized using the frequency domain amplitude integration of narrowband signals, followed by adaptive threshold correction to eliminate invalid narrowband signal weights. The adaptive weight vector improves the localization method's accuracy and interference suppression. The effectiveness and universality of the proposed method are demonstrated with test data from dry transformers and pumps, and its applicability is shown to extend to various spatial spectrum estimation algorithms and deep learning-based sound source localization techniques. • Enhanced approach for abnormal machine sound localization developed using automatic extraction and adaptive weighting. • Characteristic frequency extraction improved through refined autoregressive algorithm and density-based clustering. • Frequency mismatch in localization addressed by adaptive weighting technique. • Method's effectiveness validated with test data from various machines. • Applicability demonstrated for different spatial spectrum estimation and deep learning-based localization techniques. [ABSTRACT FROM AUTHOR]

Since CCTV is very important for indoor and outdoor security surveillance, the number of installations is increasing. However, due to the high cost of CCTV installation, there is a limit to placing it in a major location in the city center, and there is a disadvantage in that the performance is deteriorated in a dark place at night. Therefore, this paper developed an abnormal sound source detection system based on a microphone array that can compensate for the deterioration of CCTV's outdoor night security surveillance performance. Furthermore, the sound information corresponding to the location of the occurred sound source was determined whether abnormal sound is or not by using a deep learning-based abnormal sound classification technique. The performance of the proposed system was verified in various experimental environments indoors and outdoors, the sound location estimation and abnormal sound source classification were successful. [ABSTRACT FROM AUTHOR]

Tracking the flight patterns of birds and bats in three-dimensional space is central to key questions in evolutionary ecology but remains a difficult technical challenge. For example, complex aerial flight displays are common among birds breeding in open habitats, but information on flight performance is limited. Here, we demonstrate the feasibility of using a large ground-based 4-microphone planar array to track the aerial flight displays of the cryptic Jack Snipe Lymnocryptes minimus. The main element of male display flights resembles a galloping horse at a distance. Under conditions of sufficient signal-to-noise ratio and of vertical alignment with the microphone array, we successfully tracked male snipe in 3D space for up to 25 seconds with a total flight path of 280 m. The 'gallop' phase of male snipe dropped from ca. 141 to 64 m above ground at an average velocity of 77 km/h and up to 92 km/h. Our project is one of the first applications of bioacoustics to measure 3D flight paths of birds under field conditions, and our results were consistent with our visual observations. Our microphone array and post-processing workflow provides a standardised protocol that could be used to collect comparative data on birds with complex aerial flight displays. [ABSTRACT FROM AUTHOR]

Successful and efficient pest management is key to sustainable horticultural food production. While greenhouses already allow digital monitoring and control of their climate parameters, a lack of digital pest sensors hinders the advent of digital pest management systems. To close the control loop, digital systems need to be enabled to directly assess the state of different insect populations in a greenhouse. The presented article investigates the feasibility of acoustic sensors for insect detection in greenhouses. The study is based on an extensive dataset of acoustic insect recordings made with an array of high-quality microphones under noise-shielded conditions. By mixing these noise-free laboratory recordings with environmental sounds recorded with the same equipment in a greenhouse, different signal-to-noise ratios (SNR) are simulated. To explore the possibilities of this unique and novel dataset, two deep-learning models are trained on this simulation data. A simple spectrogram-based model represents the baseline for a comparison with a model capable of processing multi-channel raw audio data. Making use of the unique possibility of the dataset, the models are pre-trained on clean data and fine-tuned on noisy data. Under lab conditions, results show that both models can make use of not just insect flight sounds but also the much quieter sounds of insect movements. First attempts under simulated real-world conditions showed the challenging nature of this task and the potential of spatial filtering. The analysis enabled by the proposed methods for training and evaluation provided valuable insights that should be considered for future work. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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.)

Acoustic cameras (ACs) have become very popular in the last decade as an increasing number of applications in environmental acoustics are observed, which are mainly used to display the points of greatest noise emission of one or more sound sources. The results obtained are not yet certifiable because the beamforming algorithms or hardware behave differently under different measurement conditions, but at present, not enough studies have been dedicated to clarify the issues. The present study aims to provide a methodology to extract analytical features from sound maps obtained with ACs, which are generally only visual information. Based on the inputs obtained through a specific measurement campaign carried out with an AC and a known sound source in free field conditions, the present work elaborated a methodology for gathering the coordinates of the maximum emission point on screen, its distance from the real position of the source and the uncertainty associated with this position. The results obtained with the proposed method can be compared, thus acting as a basis for future comparison studies among calculations made with different beamforming algorithms or data gathered with different ACs in all real case scenarios. The method can be applicable to any other sector interested in gathering data from intensity maps not related to sound.

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.

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.

This paper realizes the speech source localization for microphone arrays based on the sound propagation model. According to the actual environment and location of the sound source, this paper divides the sound source into far-field source and near-field source and constructs the far-field sound model and near-field sound model applicable to the microphone array. The TDOA time-delayed localization algorithm is employed to locate the voice source of the microphone array by judging the sound far and near the field. In the localization test, this paper selects microphones to form an array according to the actual needs and preprocesses the sound signal data required for practice. The preprocessing data and sound source localization practice prove that the microphone array speech source localization algorithm used in this paper can effectively estimate the actual position of the sound source, and the absolute error between its estimated sound source position and the actual sound source position is only about 0.3m.

This study proposes a closed-form direction-of-arrival (DoA) solution derived from multilateration equations for microphone arrays of co-centered and orthogonal pairs. The generalized cross-correlation phase transform (GCC-Phat) algorithm is used to obtain the time-difference-of-arrival (TDoA) values. Simulation studies have shown the success of our proposed method compared to existing DoA methods in the literature by varying the sampling frequency of the sound signal, inter-microphone distances, and the source distance. The results from the simulation are validated by the measurements from our experiments. Our proposed solution gives better results than the far-field solution against the angle error, which is more pronounced at incidence angles smaller than 15°. These angle errors, which approach 3° using the far-field method, are reduced to less than 0.5 degrees using our proposed solution. Our solution also gives more stable results against TDoA measurement errors. Our proposed solution achieves a 66% improvement for azimuth angle and 5.88% improvement for elevation angle compared to the simulation results in the absence of TDoA measurement error, outperforming the far-field approach. When normally distributed sampling error is added to TDoA measurements, with a standard deviation of three samples, our proposed solution achieves a 41% improvement for azimuth angle and a 5.44% improvement for elevation angle. In our field measurements, an absolute mean error of 0.94 degrees was observed with our proposed method for azimuth angle. It is shown to be a more stable and faster solution method for real-time applications. [ABSTRACT FROM AUTHOR]