Sound Source Identification Research Articles

Super-resolution localization and orientation estimation of multiple dipole sound sources: From a maximum likelihood framework to wind tunnel validation

This paper investigates the identification of multiple dipole sound sources using sound pressures measured from a microphone array. The problem is addressed in the maximum likelihood (ML) framework, where the locations, orientations, and powers of multiple dipole sound sources are unknown parameters to be estimated. By the consistency property of ML, the estimated parameters converge to their actual values, which implies an asymptotically perfect spatial resolution, if a sufficiently high signal-to-noise ratio can be achieved. In order to reduce the dimension of the optimization problem of ML, the contribution of each dipole source to the measured pressures is assumed to be a latent variable and the ML problem is equivalently solved via the expectation–maximization (EM) algorithm, which iteratively and sequentially updates each source contribution and the associated sound source parameters. The number of sound sources can also be determined by the model selection approaches which add a penalty of model dimension to the ML objective function. The proposed method is assessed via a laboratory experiment where the sound field is produced by dipole speakers and a wind tunnel experiment where airframe aerodynamic noise is generated at a high Reynolds number. Experimental results show that the proposed method outperforms existing approaches in the sense of higher spatial resolution, more accurate localization, and the capacity to identify the orientations of multiple dipole sound sources.

Sound Source Identification of Vehicle Noise Based on a Microphone Array With a Modified Multiple Signal Classification Algorithm

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.

The Role of Soundscape on Urban Attractiveness in Traditional Cities

The soundscape is an immanent part of the open spaces in historical cities. This study aims to investigate the role of soundscape in enhancing urban attraction and its effect on intentional behavior. This is based on a questionnaire survey, which is conducted in compliance with ISO/TS 12913-2:2018, of 31 visitors in the Almutanabi and Almurad (alkadhmya) streets in historical blocks in Baghdad city. SPSS software is used to analyze the data. The results indicate that soundscape has a positive significance on urban attraction and the overall response to the sound source identification shows that the sound of human beings is dominant strongly while the sound of technology comes in second place and the sounds of nature are limited. In general, positive soundscape perception may contribute to the urban attraction of open spaces in historical cities.

Comparison and application of the far-field identification algorithms for multiple sound sources based on microphone array

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.

Sound source identification of a cylindrical shell by merging near-field acoustic holography with operational transfer path analysis

The purpose of this paper is to propose a new sound source identification method to identify and separate the sound sources generated by the cross-coupled vibration sources inside the cylindrical shell structure. Near-field acoustic holography (NAH) has fundamentally changed sound source identification in that it has enabled the identification of sound sources and the visualization of the 3-D sound field. Nevertheless, the NAH technique is still unable to identify the vibration sources inside a structure and also finds it difficult to identify the contribution of a single sound source to sound fields due to cross-coupling among the vibration sources. To overcome these limitations, a modified operational transfer path analysis (OPA) technique has also been proposed, which can address the cross-coupling between vibration sources. In practice, however, a single identification method often appears to be inadequate. Thus, in this paper, a novel method of merging the NAH technique and the modified OPA technique has been adopted and used to identify the structure-borne sound source of a cylindrical shell. Finally, the adaptability of the proposed method has been demonstrated by numerical simulations and experimentally and it has been shown that the novel method can not only compute the sound field distribution of a cylindrical surface, but also reconstruct other 3-D field distributions, and moreover, can locate a sound source and predict the sound field.

High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement

Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green’s function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances.

An analysis of abnormal vibration and noise caused by shaft coupling misalignment of high-speed train

This study conducts on-site testing and simulation analysis of the abnormal noise phenomenon generated during the operation of high-speed trains. The sound pressure characteristics in the cabin and the vibration of different vehicle components are recorded and subjected to comprehensive analysis. Test results indicate that significant abnormal noise occurs inside the cabin at the speed of 250 km/h, with frequencies of 87 Hz, 173 Hz, 259 Hz, and 346 Hz. Through sound source identification, vibration transmission analysis and theoretical derivation, it has been determined that the abnormal noise primarily originates from the transmission system, and it is further speculated that these abnormal vibrations and noises may be correlated with the shaft coupling misalignment within the transmission system. To verify the conjecture, a high-speed vehicle electromechanical coupling simulation model was established, incorporating the shaft coupling misalignment in the transmission system. The vibration characteristics of vehicle components were analyzed, confirming that shaft coupling misalignment leads to uneven meshing forces in gears, resulting in significant vibration and noise that affect the riding comfort of the vehicle. We propose replacing the shaft coupling to solve this problem, the test results showed that the abnormal vibration amplitudes of the transmission system at 87 Hz, 173 Hz, 259 Hz, and 346 Hz were significantly reduced after replacing the shaft coupling, and the amplitude of the corresponding frequencies on the cabin floor decreased by 91 %, 78 %, 98 %, and 66 %, respectively. The issue of abnormal noise has been effectively resolved.

Integrating multi-microphone devices and AI for noise event identification in unattended monitoring

This paper explores the use of multi-microphone devices and artificial intelligence (AI) for the identification of noise events in unattended noise monitoring. The system presented aids in the identification and localization of dominant sound sources using time difference of arrival techniques. By incorporating AI technology, the system's software automates the extraction of non-relevant sounds. The algorithms enable removal of undesired components, resulting in a more accurate measurement. This article discusses the benefits and limitations associated with employing automatic processes in noise monitoring and analysis software. Finally, a current application and prospects for future advancements in the field are presented.

Auditory hemispheric asymmetry for actions and objects.

What is the function of auditory hemispheric asymmetry? We propose that the identification of sound sources relies on the asymmetric processing of two complementary and perceptually relevant acoustic invariants: actions and objects. In a large dataset of environmental sounds, we observed that temporal and spectral modulations display only weak covariation. We then synthesized auditory stimuli by simulating various actions (frictions) occurring on different objects (solid surfaces). Behaviorally, discrimination of actions relies on temporal modulations, while discrimination of objects relies on spectral modulations. Functional magnetic resonance imaging data showed that actions and objects are decoded in the left and right hemispheres, respectively, in bilateral superior temporal and left inferior frontal regions. This asymmetry reflects a generic differential processing-through differential neural sensitivity to temporal and spectral modulations present in environmental sounds-that supports the efficient categorization of actions and objects. These results support an ecologically valid framework of the functional role of auditory brain asymmetry.

Sound source identification algorithm for compressed beamforming

Sound source identification algorithm for compressed beamforming

Multipole transfer matrix model-based sparse Bayesian learning approach for sound source identification

Accurate source identification is key to solving complex flow-induced noise problems, and sources often have non-uniform radiation patterns. At the same time, traditional methods have many application limitations, including the need for prior knowledge of the sound field, as well as inability to apply to coherent sources or coexisting multipoles. A multipole transfer matrix model-based sparse Bayesian learning approach for sound source identification (MT-SBL) is proposed. In this algorithm, the transfer relationship for the multipole radiation patterns of sound sources is established. Then, the signal sparse reconstruction is transformed into an iterative updating problem of the feature parameters under a sparse Bayesian learning (SBL) framework, which leads to the accurate identification of multipole sources. Numerical simulations and experiments on electric speakers in an anechoic environment validate the proposed algorithm. Compared with the existing methods, the algorithm is more advantageous in terms of accuracy, resolution, and computational efficiency. Further experiments were conducted on the identification of supersonic jet noise sources, verifying the effectiveness of the method for identifying strongly directional sound sources.

Fast identification of coherent sound sources with the covariance matrix fitting method based on non-synchronous measurements

The Non-Synchronous Measurements is particularly effective at expanding the operating frequency range of a single microphone array. The completed cross-spectral matrix of the synthetic array, when combined with high-resolution imaging algorithms, can yield accurate sound source identification results. However, when matrix completion algorithms are applied to large synthetic arrays, their computational complexity increases significantly. Furthermore, existing integrations with high-resolution imaging algorithms often encounter difficulties in the presence of coherent sound sources. To address this problem, this paper introduces the accelerated gradient descent algorithm for the covariance matrix fitting by orthogonal least squares. The matrix completion model for non-synchronous measurements is initially simplified through matrix decomposition, thereby enabling the rapid completion of the cross-spectral matrix using the accelerated gradient descent algorithm. This is then followed by the application of the covariance matrix fitting by orthogonal least squares to achieve quick and precise identification of coherent sound sources employing the completed cross-spectral matrix. The performance of the algorithm is evaluated using numerical simulations and validated through loudspeaker experiments in an anechoic chamber. The results from these simulations and experiments reveal that the proposed algorithm not only improves matrix completion performance on large synthetic arrays but also accurately identifies the locations and correlation coefficients of coherent sound sources.

Sound-localization-related activation and functional connectivity of dorsal auditory pathway in relation to demographic, cognitive, and behavioral characteristics in age-related hearing loss.

Patients with age-related hearing loss (ARHL) often struggle with tracking and locating sound sources, but the neural signature associated with these impairments remains unclear. Using a passive listening task with stimuli from five different horizontal directions in functional magnetic resonance imaging, we defined functional regions of interest (ROIs) of the auditory "where" pathway based on the data of previous literatures and young normal hearing listeners (n = 20). Then, we investigated associations of the demographic, cognitive, and behavioral features of sound localization with task-based activation and connectivity of the ROIs in ARHL patients (n = 22). We found that the increased high-level region activation, such as the premotor cortex and inferior parietal lobule, was associated with increased localization accuracy and cognitive function. Moreover, increased connectivity between the left planum temporale and left superior frontal gyrus was associated with increased localization accuracy in ARHL. Increased connectivity between right primary auditory cortex and right middle temporal gyrus, right premotor cortex and left anterior cingulate cortex, and right planum temporale and left lingual gyrus in ARHL was associated with decreased localization accuracy. Among the ARHL patients, the task-dependent brain activation and connectivity of certain ROIs were associated with education, hearing loss duration, and cognitive function. Consistent with the sensory deprivation hypothesis, in ARHL, sound source identification, which requires advanced processing in the high-level cortex, is impaired, whereas the right-left discrimination, which relies on the primary sensory cortex, is compensated with a tendency to recruit more resources concerning cognition and attention to the auditory sensory cortex. Overall, this study expanded our understanding of the neural mechanisms contributing to sound localization deficits associated with ARHL and may serve as a potential imaging biomarker for investigating and predicting anomalous sound localization.

Synchronizing Automatic Gain Control in Bilateral Cochlear Implants Mitigates Dynamic Localization Deficits Introduced by Independent Bilateral Compression.

The independence of left and right automatic gain controls (AGCs) used in cochlear implants can distort interaural level differences and thereby compromise dynamic sound source localization. We assessed the degree to which synchronizing left and right AGCs mitigates those difficulties as indicated by listeners' ability to use the changes in interaural level differences that come with head movements to avoid front-back reversals (FBRs). Broadband noise stimuli were presented from one of six equally spaced loudspeakers surrounding the listener. Sound source identification was tested for stimuli presented at 70 dBA (above AGC threshold) for 10 bilateral cochlear implant patients, under conditions where (1) patients remained stationary and (2) free head movements within ±30° were encouraged. These conditions were repeated for both synchronized and independent AGCs. The same conditions were run at 50 dBA, below the AGC threshold, to assess listeners' baseline performance when AGCs were not engaged. In this way, the expected high variability in listener performance could be separated from effects of independent AGCs to reveal the degree to which synchronizing AGCs could restore localization performance to what it was without AGC compression. The mean rate of FBRs was higher for sound stimuli presented at 70 dBA with independent AGCs, both with and without head movements, than at 50 dBA, suggesting that when AGCs were independently engaged they contributed to poorer front-back localization. When listeners remained stationary, synchronizing AGCs did not significantly reduce the rate of FBRs. When AGCs were independent at 70 dBA, head movements did not have a significant effect on the rate of FBRs. Head movements did have a significant group effect on the rate of FBRs at 50 dBA when AGCs were not engaged and at 70 dBA when AGCs were synchronized. Synchronization of AGCs, together with head movements, reduced the rate of FBRs to approximately what it was in the 50-dBA baseline condition. Synchronizing AGCs also had a significant group effect on listeners' overall percent correct localization. Synchronizing AGCs allowed for listeners to mitigate front-back confusions introduced by unsynchronized AGCs when head motion was permitted, returning individual listener performance to roughly what it was in the 50-dBA baseline condition when AGCs were not engaged. Synchronization of AGCs did not overcome localization deficiencies which were observed when AGCs were not engaged, and which are therefore unrelated to AGC compression.

Unveiling the impact of auditory salience and sources identification on perceived pleasantness in environmental soundscapes

On the basis of initial results related to modulations of auditory salience— the ability to capture attention—by timbre attributes (especially, brightness and roughness), the present study investigates auditory salience as a predictor of perceived pleasantness in environmental sound scenes. A new paradigm was set up to measure continuous pleasantness while observing the impact of specific events, in a corpus of 11 various soundscapes. Specific events were found to affect perceived pleasantness, in a source-dependent direction in line with the literature. In addition, causality analyses examined the impact of temporal salience predictions on pleasantness ratings, along with other indices (equivalent sound level, loudness, or timbre attributes). They showed that salience was the best predictor of pleasantness, ahead of loudness, level or brightness and roughness. Nonetheless, the last two turned out to play, as well, a significant role in the pleasantness percept. Therefore, auditory salience is confirmed as a relevant indicator for assessing the perception of soundscapes, and the identification of sound sources that compose them is also confirmed to be crucial in this assessment.

Physics informed sound field interpolation using an acoustic sensor network

Sound field estimation is the process of analyzing and characterizing the distribution of sound waves in a particular physical space. The applications of sound field estimation extend to various areas, including the visualization of acoustic fields, interpolation of room impulse responses, identification of sound sources, capturing sound fields for spatial audio, and spatial active noise control, among other potential uses. In a previous study, a directionally weighted kernel has been used to estimate the sound field, where the priori information of source directions is employed to improve the estimation accuracy. In another separate study, spherical harmonics have been used to represent the sound field. However, the order of spherical harmonic coefficients was limited due to the limited number of microphones. This research introduces a novel method for sound field estimation using multiple microphones to sample a source-free volume. A physics-informed neural network is used to predict the spherical harmonics coefficients and locations of unknown sources to estimate the sound field.

Surface and Underwater Acoustic Source Recognition Using Array Feature Extraction Based on Machine Learning

Sound source recognition is an important application of passive sonar. How to distinguish between surface and underwater sound sources is always a difficult problem. In order to solve the problem of S/U sound source identification, this paper proposes a machine learning method based on array feature extraction, which has certain innovative value. Firstly, according to the experimental environment of SACLANT 1993, simulation data is generated based on KRAKEN. Secondly, the simulation data and experimental data are used to extract the array features. Thirdly, the accuracy, recall rate, F1 and accuracy of GBDT classifier in three different frequency bands are evaluated. The results show that the training model established by using the array feature extraction method can effectively solve the problem of poor accuracy of some channels in the single channel classification, and obtain good experimental accuracy. Finally, the experimental accuracy of the three bandwidths is 0.9920, 0.9857 and 0.9713, respectively.

A method of sound source recognition based on two stagewise weak orthogonal matching pursuit

The stagewise weak orthogonal matching pursuit (SWOMP) algorithm solves the problem that the orthogonal matching pursuit (OMP) algorithm relies too much on the sparsity of sound sources in the process of sound source identification. However, compared with OMP algorithm, SWOMP algorithm has some disadvantages, such as lower spatial resolution and worse identification accuracy. To solve the problem of poor reconstruction effect of SWOMP algorithm, a sound source identification algorithm based on two stagewise weak orthogonal matching pursuit (TSWOMP) is proposed. In this method, the initial atom set is obtained by threshold selection, the reliability of the primary atom set is detected, and the wrong atoms in the primary atom set are further deleted. Finally, the position and size of the sound source signal are identified according to the obtained final atom set. Under different frequencies and signal-to-noise ratios, the simulation results show that compared with SWOMP algorithm, TSWOMP algorithm can effectively reduce the main lobe width, suppress the influence of side lobes and significantly improve the spatial resolution. Moreover, even if the sparsity of sound source is unknown, it can achieve the same recognition accuracy as OMP algorithm and has high recognition stability. Finally, the validity and reliability of TSWOMP algorithm are further verified by experiments.

Numerical research on the focusing characteristics of steady state sound field in finite space by combining finite element and phase conjugation methods

The research on sound source identification in an infinite free sound field has been relatively in-depth, but there is relatively little research on sound source identification and localization in limited spaces, especially those with sound absorption and insulation materials. The identification and reconstruction of the acoustical steady propagation field radiated from a single frequency finite space are studied numerically using discrete elements based on the phase conjugation method by FEM. Two different kinds of array forms of phase conjugation arrays are studied for sound source localization such as the planar array and the linear array. In addition, the influences of the existence of sound-absorbing material on the wall on the focusing properties are also discussed. The numerical results show that: The phase conjugation method can completely achieve the identification and location of the acoustical source finite space no matter using the planar array form or the linear array according to FEM. The linear array form can obtain the subwavelength focusing with fewer elements. The optimal distance between the array and the sound source is 0.5 [Formula: see text] and 2 [Formula: see text] to get the best reconstruction results. The smaller the absorption coefficient, the more to meet the multi-path phase compensation principle, the better reconstruction of the sound source.

Noise Mapping and Measurements for Sound Environment Assessment in a Typical Chinese Water-Rich Village

The construction of beautiful villages in China is carrying more and more demand for leisure and health care, thus increasing requirements for the quality of the village environment. In this paper, Huanggongwang in Fuyang, Zhejiang Province in China, a typical water-rich village, was selected as the research object to study the sound environment quality by adapting noise mapping and field measurements. A village acoustic simulation model was established by the layout of the villages, roads, and water system; the three-dimensional spatial distribution of sound pressure level was based on actual sound source identification and measurements for sound environment evaluation. The result shows that the combination of the arithmetic mean value method for sound pressure level and noise pollution index approach can better represent the characteristics of sound environment quality compared with traditional noise evaluation standards. The average sound pressure level measured on-site in the village was 62.4 dB(A), and the PN index was 0.83, which reveals the importance of resolving the issue of environmental noise in the villages. Moreover, this evaluation method excludes the chance of actual measurement results. Therefore, it provides a better measure of the quality of the rural sound environment, which serves as a basis for the scientific planning of rural construction.