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Reverberation is the main background interference in active sonar and seriously interferes with the extraction of the target echo. Active sonar systems can use short-pulse continuous wave (CW) signals to reduce the reverberation intensity. However, as the pulse width of the CW signals decreases, the reverberation envelope exhibits a high-frequency oscillating phenomenon. Active sonar often uses the cell average constant false alarm ratio (CA-CFAR) method to process the reverberation, which steadily decays with transmission distance. However, the high-frequency oscillation of the reverberation envelope deteriorates the performance of CA-CFAR, which causes a higher false alarm rate. To tackle this problem, the formation mechanism of the high-frequency oscillation characteristics of the reverberation envelope of the short-pulse-width CW signals is modeled and analyzed, and on this basis, an α filter is designed to suppress the high-frequency oscillation of the reverberation envelope before applying CA-CFAR. The simulation and lake trial results indicate that this method can effectively suppress high-frequency oscillations of the reverberation envelope, as well as exhibit robustness and resistance to reverberation interference. [ABSTRACT FROM AUTHOR]

This article delves into Ibn 'Arabī's perspective on translation, specifically in the context of Sufi philosophy. It highlights the significance of understanding an author's worldview and reality to accurately convey their intentions. The concept of tarjama, meaning "translator" or "interpreter," is explored, particularly in relation to divine speech and words. Ibn 'Arabī refers to himself and the Prophet as tarjumān, or "Translators of the Real," who transmit God's words. The article suggests that being a Translator of the Real necessitates a state of nothingness before God and purity of the heart. It also discusses the role of the Prophet Muhammad as a translator of the Quran, emphasizing the need for purity of both body and heart to effectively convey its meanings. The text further explores the idea that all things in the cosmos are manifestations of God's speech, making translation a continuous process that requires careful listening and the removal of veils obstructing our perception of the divine address. The ability to convey suprasensory realities depends on our purity and proximity to God, as God never ceases speaking. [Extracted from the article]

Perfectly diffuse sound fields play an important role in architectural acoustics and there are established theoretical characterizations of perfect diffuseness. Although sound fields in real rooms are diffuse to some extent, they are not perfectly diffuse, and therefore theories are required to describe pseudo-perfectly diffuse sound fields. Here, we aim to spatially characterize pseudo-perfect diffuseness via directional characterization of that, finite-degree spherical harmonic diffuseness. Our results show that finite-degree diffuse sound fields yield local spatial diffuseness, suggesting that spatial pseudo-perfect diffuseness is characterized using the effective radius of diffuseness. [ABSTRACT FROM AUTHOR]

Automatic phoneme boundary detection is a key problem in speech processing and applications. The accurate phoneme segmentation in continuous speech contributes to the improvement of recognition quality. This study proposes an efficient temporal features-based approach for the automatic detection and masking of phoneme boundaries in speech signals. Changes in speech waveform during a well-spoken word can be used to identify phonemes boundaries. The signal level properties of the speech transitions in the waveform during the transformation from one phoneme to the next are analysed to establish the phoneme boundaries of a speech signal. The short time energy, pitch, zero-crossing rate, and Teager energy operator are used to determine the region of change from unvoiced to voiced and vice versa. The technique proposed in this paper enumerates empirical estimations observed with desired statistical properties for speech signal during phoneme transformation. The results of this study's demonstrate that precise phoneme boundaries can be obtained by using the proposed method. The proposed method is applied on the TIMIT corpus and achieved the accuracy of 96.04% is accomplished within a tolerance scale of 10 ms. [ABSTRACT FROM AUTHOR]

Image dehazing is one of the pivotal topics in computer vision arena due to its significant effects on improving the foggy images that taken under poor weather conditions. Problems such as halo outliers and over-saturation are presented in the ridges i.e. high-frequency components of the hazy images which lead to false guesstimate of atmospheric light and transmission depth that negatively results on the task of visibility restoration image. In this paper, the proposed method has the ability to deal with hazy images with several haze density while most of state-of-the-art dehazing techniques suffer from lack of contrast of the observed object in the dehazed images due to unequal distribution of transmission depth. In order to achieve high visual quality from the contaminated hazy images, the proposed method is designed according to semi soft thresholding and Gamma transformation where the second-generation wavelet transformation is used to improve the transmission map. Qualitative and quantitative results showed that, the proposed method outperforms sophisticated state-of-the-art image haze removal techniques both in visual image quality and time efficiency. [ABSTRACT FROM AUTHOR]

Speech is produced by a nonlinear, dynamical Vocal Tract (VT) system, and is transmitted through multiple (air, bone and skin conduction) modes, as captured by the air, bone and throat microphones respectively. Speaker specific characteristics that capture this nonlinearity are rarely used as stand-alone features for speaker modeling, and at best have been used in tandem with well known linear spectral features to produce tangible results. This paper proposes Recurrent Plot (RP) embeddings as stand-alone, non-linear speaker-discriminating features. Two datasets, the continuous multimodal TIMIT speech corpus and the consonant-vowel unimodal syllable dataset, are used in this study for conducting closed-set speaker identification experiments. Experiments with unimodal speaker recognition systems show that RP embeddings capture the nonlinear dynamics of the VT system which are unique to every speaker, in all the modes of speech. The Air (A), Bone (B) and Throat (T) microphone systems, trained purely on RP embeddings perform with an accuracy of 95.81%, 98.18% and 99.74%, respectively. Experiments using the joint feature space of combined RP embeddings for bimodal (A–T, A–B, B–T) and trimodal (A–B–T) systems show that the best trimodal system (99.84% accuracy) performs on par with trimodal systems using spectrogram (99.45%) and MFCC (99.98%). The 98.84% performance of the B–T bimodal system shows the efficacy of a speaker recognition system based entirely on alternate (bone and throat) speech, in the absence of the standard (air) speech. The results underscore the significance of the RP embedding, as a nonlinear feature representation of the dynamical VT system that can act independently for speaker recognition. It is envisaged that speech recognition too will benefit from this nonlinear feature. [ABSTRACT FROM AUTHOR]

This essay examines the significance of sound in religion in the context of the British debate about the French Revolution. It analyses loyalist sermons and pamphlets to show that anxieties about plebeian noise exacerbated concerns about religious enthusiasm and its relationship to political radicalism. Accordingly, the acoustic aspects of worship became an issue in conservative propaganda, especially the writings of Anglican clergymen. By contrast, Christian reformers found the sound of enthusiastic religion a source of empowerment. Focusing on prophecies, hymns and sermons, this essay argues that a group of plebeian Dissenters, whose piety shaped their commitment to reform, attributed a revolutionary meaning to the sonic intensity of religious enthusiasm. [ABSTRACT FROM AUTHOR]

Predicting sound sources in reverberant environments is a challenging task because reverberation causes reflection and scattering of sound waves, making it difficult to accurately determine the position of the sound source. Due to the characteristics of overcoming multipath effects and adaptive focusing of the time reversal technology, this paper focuses on the application of the time reversal operator decomposition method for sound source localization in reverberant environments and proposes the image-source time reversal multiple signals classification (ISTR-MUSIC) method. Firstly, the time reversal operator is derived, followed by the proposal of a subspace method to achieve sound source localization. Meanwhile, the use of the image-source method is proposed to calculate and construct the transfer matrix. To validate the effectiveness of the proposed method, simulations and real-data experiments were performed. In the simulation experiments, the performance of the proposed method under different array element numbers, signal-to-noise ratios, reverberation times, frequencies, and numbers of sound sources were studied and analyzed. A comparison was also made with the traditional time reversal method and the MUSIC algorithm. The experiment was conducted in a reverberation chamber. Simulation and experimental results show that the proposed method has good localization performance and robustness in reverberant environments. [ABSTRACT FROM AUTHOR]

According to its cultural and spiritual importance, church acoustics are intensively investigated. These architectural marvels serve religious worship, community events, and cultural preservation. Preserving these sites' historical importance and acoustic comfort requires understanding their complex sound transmission dynamics. However, urban noise severely reduces sacred sites acoustically. In view of their proximity to airports, highways, and Metro, this study explores the acoustics of Chennai's medieval St Thomas Garrison Church. Reverberation times (RT) at 500 and 1000 Hz exceed the optimum value, especially for speech purposes. Day and night background noises over 60 dBA affect speech transmission index (STI) and sound pressure level (SPL) distribution. These findings are also examined in relation to potential service interventions, such as amplification systems, in places of worship. This study suggests that urban planners, architects, and others can help preserve and improve religious experiences by addressing urban noise and improving church acoustics. [ABSTRACT FROM AUTHOR]

Near IR spectroscopic reverberation of Active Galactic Nuclei (AGN) potentially allows the infrared (IR) broad line region (BLR) to be reverberated alongside the disc and dust continua, while the spectra can also reveal details of dust astro-chemistry. Here, we describe results of a short pilot study (17 near-IR spectra over a 183 d period) for Mrk 509. The spectra give a luminosity-weighted dust radius of 〈 R d,lum〉 = 186 ± 4 light-days for blackbody (large grain dust), consistent with previous (photometric) reverberation campaigns, whereas carbon and silicate dust give much larger radii. We develop a method of calibrating spectral data in objects where the narrow lines are extended beyond the slit width. We demonstrate this by showing our resultant photometric band light curves are consistent with previous results, with a hot dust lag at >40 d in the K band, clearly different from the accretion disc response at <20 d in the z band. We place this limit of 40 d by demonstrating clearly that the modest variability that we do detect in the H and K band does not reverberate on time-scales of less than 40 d. We also extract the Pa β line light curve, and find a lag which is consistent with the optical BLR H β line of ∼70–90 d. This is important as direct imaging of the near-IR BLR is now possible in a few objects, so we need to understand its relation to the better studied optical BLR. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the characteristics and the formation mechanism of reverberation is the key to improving the performance of the active target detection. In response to the challenge of analyzing the intensity of bottom reverberation in typical deep-sea environments, this study proposes a prediction method for the bottom reverberation intensity under beam-controlled emission conditions. It explains the variation law of bottom reverberation intensity under beam-controlled emission conditions in typical deep-sea environments of the South China Sea through theoretical and simulation analyses. Reverberation intensity of the deep-sea bottom under beam-controlled emission conditions exhibits significant fluctuations during the duration of reverberations in the direct sound zone of the seabed. This phenomenon is closely related to the directionality of the source emission, leading to intermittent reverberation masking and detectable areas in the active sonar detection. In addition, the duration of the high-reverberation zone near the cutoff distance of the direct sound from the seabed is longer under the beam-controlled emission conditions of the emission array located within the surface waveguide layer of the deep sea during winter. [ABSTRACT FROM AUTHOR]

For health and impact studies of water systems, monitoring underwater environments is essential, for which multi-frequency single- and multibeam echosounders are commonly used state-of-the-art technologies. However, the current scarcity of sediment reference datasets of both bottom backscatter angular response and water column scattering hampers empirical data interpretation. Comprehensive reference data derived from measurements in a controlled environment should optimize the use of empirical backscatter data. To prepare for such innovative experiments, we conducted a feasibility experiment in the Delta Flume (Deltares, The Netherlands). Several configurations of sonar data were recorded of the flume floor and suspended sediment plumes. The results revealed that flume reverberation was sufficiently low and that the differential settling of fine-sand plumes in the water column was clearly detected. Following this successful feasibility test, future comprehensive experiments will feature multi-frequency multi-angle measurements on a variety of sediment types, additional scatterers and sediment plumes, resulting in reference datasets for an improved interpretation of underwater backscatter measurements for scientific observation and sustainable management. [ABSTRACT FROM AUTHOR]

This document provides an overview of recent advancements in audio signal processing. The use of artificial intelligence (AI) and deep learning technologies has greatly improved the analysis and interpretation of audio signals. These advancements have led to progress in areas such as speaker recognition, sound categorization, emotional computing, and noise reduction. The document highlights several research articles that explore different aspects of audio signal processing, including the effects of noise on gas discharges, deep learning-based detection of voice pathology (specifically COVID-19), estimating the impact of room acoustics on sound signals, analyzing the performance of vacuum cleaners based on sound analysis, incomplete sound event detection, analyzing dolphin whistles, high-speed pitch detection, acoustic imaging for spatially mapping sounds, enhancing personal audio systems with sound field control technology, and audio classification in construction sites. These advancements demonstrate the potential of audio processing and analysis techniques in various fields. [Extracted from the article]

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]

Aiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHAT algorithm to calculate the response power spectra of the collected multichannel fault signals. Through the utilization of the U-net neural network, the response power spectra containing spurious peaks are transformed into "clean" estimated source distribution maps. By employing interpolation search, the estimated source distribution maps are processed to obtain location estimations for multiple fault sources. To validate the effectiveness of the proposed method, this paper constructs an experimental dataset using mechanical fault data from electromechanical equipment relays and conducts sound source localization experiments. The experimental results show that the U-net network under 0.2 s/0.5 s/0.7 s reverberation time can effectively eliminate spurious peak interference in the response power spectrum. As the signal-to-noise ratio decreases, it can still distinguish the sound sources with a distance of 0.2 m. In the context of multifault source localization, the method is capable of simultaneously locating the positions of four fault sources, with an average localization error of less than 0.02 m. The method in this paper effectively eliminates spurious peaks in the response power spectra under conditions of multisource strong reverberation. It accurately locates multiple mechanical fault sources, thereby significantly enhancing the efficiency of mechanical fault detection. [ABSTRACT FROM AUTHOR]

Underwater images typically suffer from color distortion and low contrast, owing to the light absorption and scattering. To handle the visual manifestation of such degraded images, numerous underwater image dehazing algorithms have been presented. However, most of existing dehazing methods still have room for improvement in terms of preserving more details in the restored results. In this paper, we propose a simple yet effective dehazing method. The core idea is to produce the high-quality underwater images with rich detail information and vivid color. Firstly, an innovative color correction is designed to compensate the information of each color components. This operation is a pre-processing procedure, in which, selective absorption is fully considered. Then a dual transmission map-based haze removal method is introduced. Different from previous methods, a novel color channel with two terms is constructed to accurately estimate the transmission maps. The one is designed as the sharpened term to reveal more image detail and edges. The other is a difference of channel intensity prior term to remove the influence of light scattering. By using this strategy, our method can generate a natural appearance of the restored image with more detail information and higher color contrast. Experiments on representative images have proven that the performer of our method is 8.6% and 2.7% better than the second best on the average scores of patch-based contrast quality index (PCQI) and Entropy metrics, respectively. [ABSTRACT FROM AUTHOR]

The steered response power (SRP) with phase transform algorithm has been demonstrated to be robust against reverberation and noise for single-source localization. However, when this algorithm is applied to multisource localization (MSL), the "peak missing problem" can occur, namely, that some sources dominate over others over short time intervals, resulting in fewer significant SRP peaks being found than the true number of sources. This problem makes it difficult to detect all the sources among the available SRP peaks. We propose an iteratively reweighted steered response power (IR-SRP) approach that effectively solves the "peak missing problem" and achieves robust MSL in reverberant noisy environments. The initial IR-SRP localization function is computed over the time-frequency (T-F) bins selected by a combination of two weighting schemes, one using coherence, and the other using signal-to-noise ratio. When iterating, our method finds the significant SRP peaks for the dominant sources and eliminates the T-F bins contributed by these sources using inter-channel phase difference information. As a result, the remaining sources can be found in subsequent iterations among the remaining T-F bins. The proposed IR-SRP method is demonstrated using both simulated and measured experiment data. [ABSTRACT FROM AUTHOR]

Two measures of auditory spatial resolution, the minimum audible angle and the minimum audible movement angle, have been obtained in a simulated acoustic environment using Ambisonics sound field reproduction. Trajectories were designed to provide no reliable cues for the spatial discrimination task. Larger threshold angles were found in reverberant compared to anechoic conditions, for stimuli on the side compared to the front, and for moving compared to static stimuli. The effect of reverberation appeared to be independent of the position of the sound source (same relative threshold increase) and was independently present for static and moving sound sources. [ABSTRACT FROM AUTHOR]

The Namakhvani Hydro Power Plant (HPP) loomed large, not just as an ambitious energy project, but as a potential paradigm shift for Georgia's economic landscape. This article delves into the intricate machinery of the project, from its initial aspirations to its unforeseen derailment. It dissects the economic repercussions of its abandonment, casting a dark shadow on the nation's investment climate. Beyond the concrete details, the paper delves into the critical anatomy of the Namakhvani saga. What factors conspired to prevent its completion? What crucial lessons can be gleaned from this missed opportunity? To answer these questions, the article ventures beyond Georgia's borders, exploring successful HPP projects in other countries, and meticulously analyzing their blueprints and community engagement strategies. Drawing from both domestic and international experiences, it paves the way for a revised roadmap for future endeavors. Namakhvani stands not as a monument to failure, but as a potent catalyst for introspection and improvement. This article serves as a critical mirror, reflecting not just the technical missteps but also the fundamental need for transparency, public trust, and rigorous environmental assessments. It champions the integration of local expertise and international best practices, advocating for a collaborative approach that prioritizes both economic progress and ecological responsibility. [ABSTRACT FROM AUTHOR]

The core‐mantle boundary (CMB) marks the most dramatic changes in physical properties within the Earth, and plays a critical role in the understanding of the Earth's dynamics. PmKP waves are seismic phases that reflect (m − 1) times under the CMB and are useful for studying the complex CMB structure. We present an automated workflow for detecting PmKP phases using multi‐station records from global seismic stations. We employ a novel sampling method to extract PmKP waveforms into a 2‐D matrix. Two deep neural networks are then utilized for initial phase detections and subsequent slowness validations. Numerous PmKPab (3 ≤ m ≤ 7) and their CMB diffracted signals were identified for deep earthquakes (magnitude >6) occurred from 2000 to 2020, including diffracted P7KPab waves with diffraction lengths of nearly 20°. Our approach significantly improves the efficiency of PmKP phase identification and holds the capability to detect other weak core phases, such as PKiKP. Plain Language Summary: Seismic waves provide invaluable information about the Earth's core when sampling the core‐mantle boundary (CMB) complex structures. However, adequate sampling of this region remains a challenge due to the uneven distribution of earthquakes and seismic stations. PmKP is a seismic phase that reflects multiple times (m − 1) within the CMB, and even though it has been considered elusive, it offers an opportunity to overcome the sampling limitation caused by the earthquake‐station distribution and enhance the CMB seismic coverage. Here, we propose a new automated approach to detect unprecedent high reverberations of PmKP. Our method utilizes a combination of two deep neural networks to analyze seismic records from stations distributed globally. By applying this approach to seismic records between 2000 and 2020, we successfully detected up to six underside reflections of PmKP phases and their CMB diffracted waves. Our method enhances the efficiency of identifying PmKP phases and has the potential to be applied to other core seismic phases. Key Points: We propose a workflow based on neural networks to detect rarely observed core seismic phases and their diffractionsOur method successfully detects PmKP up to six underside reflections on the core‐mantle boundary (CMB), increasing the seismic sampling of the CMBThe new workflow can be used for future automated identification of other rare or weak seismic phases [ABSTRACT FROM AUTHOR]

Several billionaires have recently emerged as leaders of climate governance. So far, little research has examined how they legitimize their involvement in climate networks. We argue that billionaire governance entrepreneurs have high levels of resources but low procedural legitimacy. They pursue output legitimacy to support their political action, highlighting their effectiveness in managing climate issues. Their main strategies, depoliticization, outgrouping, and technical solutionism, may give them short-term legitimacy but risk undermining their long-term goals of addressing climate change. We analyze the discursive legitimation strategies of a successful billionaire entrepreneur in transnational climate governance, Michael Bloomberg. Our empirical analysis is based on the study of more than 800 statements, speeches, and news releases related to Bloomberg's climate action from 2010 to 2021. It contributes to the study of entrepreneurship, leadership, philanthropy, and transnational actors in climate governance. [ABSTRACT FROM AUTHOR]

Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P‐waves and 0.12 s for S‐waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments. Plain Language Summary: Ocean bottom seismometers (OBS) are seismic stations on the seafloor. Just like their counterparts on land, they record many earthquakes on three component sensors but are additionally equipped with underwater hydrophones. To determine the location of an earthquake, seismologists must precisely measure the arrival times of seismic waves. For onshore data, machine learning (ML) has been highly successful in determining earthquake arrival times. However, the noise and the signal are different in the ocean environment. For example, the recordings can contain whale songs and water layer reverberations and are disturbed by ocean bottom currents. We have assembled an extensive database of ocean bottom recordings and trained artificial neural networks to use the underwater hydrophone information and cope with the ocean noise environment. We demonstrate that the resulting ML picker picks are similar to those of human experts and outperform phase pickers based on land data only. We compare earthquake catalogs based on different pickers created from an OBS deployment offshore New Zealand and demonstrate that PICKBLUE outperforms previous pickers. We make the database and ML picker available with a standard interface so that it is easy for other scientists to apply them in their studies. Key Points: We assembled a database of ocean Bottom Seismometer (OBS) waveforms and manual P and S picks, on which we train PickBlue, a deep learning pickerOur picker significantly outperforms pickers trained with land‐based data with confidence values reflecting the likelihood of outlier picksThe picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records [ABSTRACT FROM AUTHOR]

This paper presents two solutions employing locally resonant metamaterial to level the mass-airmass resonance impact on the sound insulation. The first operates on the cantilever beam resonance, and the second uses masses vibrating in flexural mode cut out from the additional panel. Both structures are mounted between two lightweight, honeycomb cardboard panels with a double-wall resonance of 420 Hz. Solutions were analysed numerically for their vibration and acoustic properties and measured in the reverberation chamber, resulting in information about the dispersion curve, effective dynamic mass, and sound insulation. The analytical results of Sound Transmission Loss (STL) and the experimental measurements of diffused-field Sound Reduction Index (SRI) proved the existence of sound-insulation enhancement. The local rise in SRI resulted in an increase of broadband Weighted SRI up to 5 dB. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for detecting wave packets in a circular waveguide. The waveguide terminated with a concrete plug was used to test the method. The concrete was made in accordance with the Eurocode standard. During the tests, a significant difference was observed between the behavior of the speaker and the concrete plug. The pulse reflected from the plug maintained its shape regardless of the sound level. The reflection of the pulse from the speaker's diaphragm resulted in a significant change in the form and duration of the wave packet. These changes were dependent on the sound level of the wave packet. As a result of these modifications was a significant difference between the measurement uncertainty of detecting a pulse reflected from the concrete and the speaker. In the case of reflection from the speaker, an uncertainty of 0.036% was obtained. The smallest measurement error value for the pulse reflected from the speaker was 2.5%. [ABSTRACT FROM AUTHOR]

This article focuses on a noise of aerodynamic origin, generated by the flow over single and multiple rectangular cavities. The paper presents the methodology and results of the conducted numerical simulations of the air flow in a channel with a set of rectangular cavities. The aeroacoustic wave equation was used to determine the acoustic pressure generated by the flow. Various configurations of the cavities made it possible to study the influence of their reciprocal location on the generated sound. The research showed that as the distance between the cavities decreased, the acoustic pressure levels increased. They were several decibels higher than for the single-cavity case. [ABSTRACT FROM AUTHOR]

The paper presents acoustic studies of a multilayer composite made based on Synolite 1967-G-1 polyester resin and glass fabric with a three-way arrangement of +/-45° fibres, Triaxial type and a weight of 860 g/m2. As an addition, rubber recyclate created in the process of disposal of car tires was used. The material was made using the vacuum infusion method. By the vacuum lamination method, composite materials were produced in the form of plates with the addition of rubber recyclate in four variants of the amount of recyclate used in the produced composite - 20%, 30%, 40% and 50%. Each variant contained 6 layers of fabric and five layers of rubber recyclate. The samples were tested in the vibroacoustic laboratory on a dedicated measuring station of a set of reverberation chambers without flanking transmission, in which the specific acoustic insulation Rw was determined following PN-EN ISO 10140-2 and related standards, i.e. PN-EN ISO 10140-1, PN-EN ISO 10140-2, PN-EN ISO 10140-4, PN-EN ISO 10140-5 and PN EN ISO 717-1. All manufactured variants of the composite material with the addition of rubber recyclate were characterized by acoustic insulation in the range of Rw = (30.0 ÷ 35.7) dB. High sound insulation parameters for tested panels up to 12 mm thick act as a barrier to counteracting the spread of unwanted airborne noise. Combined with good mechanical properties such as tensile strength, toughness and hardness, they form the basis for the easy design of all shields in many industries. The use of rubber recyclate obtained in the process of disposal of car tires has a positive impact on improving environmental protection. [ABSTRACT FROM AUTHOR]

Ambient infrasound noise contains an abundance of information that is typically overlooked due to limitations of typical infrasound arrays. To evaluate the ability of large‐N infrasound arrays to identify weak signals hidden in background noise, we examine data from a 22‐element array in central Idaho, USA, spanning 58 days using a standard beamforming method. Our results include nearly continuous detections of diverse weak signals from infrasonic radiators, sometimes at surprising distances. We observe infrasound from both local (8 km) and distant (195 km) waterfalls. Thunderstorms and earthquakes are also notable sources, with distant thunderstorm infrasound observed from ∼800 to 900 km away. Our findings show that large‐N infrasound arrays can detect very weak signals below instrument and environmental noise floors, including from multiple simultaneous sources, enabling new infrasound monitoring applications and helping map the composition of background noise wavefields. Plain Language Summary: Infrasound, or sound with a frequency below what humans can generally hear, is often produced by various earth processes. Compared to audible sound, infrasound can travel longer distances while retaining more energy, making it especially useful for remote monitoring purposes. Infrasound arrays are stations that consist of multiple infrasonic microphones, which use time‐delays between sensors to determine the direction of a wave's arrival. Using a greater number of microphones allows for the improved detection of weak signals, a technique referred to as "large‐N." Our study uses an arrangement of 22 microphones to locate sources of infrasound, such as waterfalls, thunderstorms, and earthquakes. Infrasound from waterfalls was identified at both nearby and distant locations, reaching almost 200 km. Thunderstorms and earthquakes were also recorded, with thunder infrasound coming from distances of nearly 900 km. Our results show that large‐N infrasound can reveal earth processes that may otherwise go unnoticed. Key Points: A 22‐element infrasound array is used to identify weak acoustic sources that could otherwise be hidden in background noiseInfrasound from thunderstorms, earthquakes, and both local and distant waterfalls is observedLarge‐N infrasound monitoring enables detection of distant waterfall infrasound, with potential applications in hydrologic monitoring [ABSTRACT FROM AUTHOR]

Aimed at image distortion caused in process of defogging images and interfered by bright objects, a single adaptive image defogging method based on sky segmentation is proposed in this paper. Firstly, the combination of the dark channels and bright channels is used to estimate the atmospheric light. And then the sky region and the accurate atmospheric light are obtained by the information entropy and the dark channel prior. Next, the initial transmittance is estimated by the dark channel prior, and then the fast guiding filter is utilized to estimate the transmittance accurately. Further, an adaptive weight factor and an error amplification compensation factor of bright objects are introduced to optimize the transmittance mapping constraint and to correct the sky region and the non-sky region, respectively. Finally, bright adjustments are performed on the fog-free image obtained from the atmospheric scattering model by a nonlinear mapping method, which causes a clearer and more natural defogging image to be got. The experimental results show that adaptive single image defogging algorithms proposed in this paper has the higher value of peak signal-to-noise ratio, the higher value of structural similarity index measure, the higher value of naturalness image quality evaluator, the higher value of fog aware density evaluator, and the less time consumed, which illustrates that the adaptive single image defogging algorithm proposed in this paper has a better defogging effect on haze images with sky areas and bright objects. [ABSTRACT FROM AUTHOR]

Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors such as the context, geography, socio-political climate, and background of the producers and consumers of the posts play a crucial role in determining if the content can be flagged as toxic. Adoption of automated toxicity detection models in production can thus lead to a sidelining of the various groups they aim to help in the first place. It has piqued researchers' interest in examining unintended biases and their mitigation. Due to the nascent and multi-faceted nature of the work, complete literature is chaotic in its terminologies, techniques, and findings. In this article, we put together a systematic study of the limitations and challenges of existing methods for mitigating bias in toxicity detection. We look closely at proposed methods for evaluating and mitigating bias in toxic speech detection. To examine the limitations of existing methods, we also conduct a case study to introduce the concept of bias shift due to knowledge-based bias mitigation. The survey concludes with an overview of the critical challenges, research gaps, and future directions. While reducing toxicity on online platforms continues to be an active area of research, a systematic study of various biases and their mitigation strategies will help the research community produce robust and fair models. [ABSTRACT FROM AUTHOR]

This study presents an approach to better characterize the P‐wave and S‐wave velocity structure of the seafloor sediment layer using ocean bottom seismometers. The presence of low‐velocity seafloor sediment layers influences the observed seismic record at the seafloor over a broad frequency range, such that detailed knowledge of this sediment structure is essential to predict its effect on teleseismic records. We use the radial component of teleseismic P waves and autocorrelation functions of the radial, vertical, and pressure components of teleseismic P and S waves to obtain sediment layer models using the Markov chain Monte Carlo approach with parallel tempering. Synthetic tests show that the body waves constrain the P‐ and S‐wave impedances and travel times and the P‐ to S‐wave velocity ratio of the sediment layers. The proposed method resolves thin layers at a high resolution, including the uppermost thin (∼50 m to a few hundred meters) low S‐wave velocity layer. Real data applications at sites across the Pacific Ocean that are coincident with previous in situ studies demonstrate the effectiveness of this method in characterizing the seafloor sediment unit. The sediment models characterized by this new approach will allow us to more accurately predict and correct the effects of sediment layers in generating P‐ and S‐wave reverberations. Plain Language Summary: Ocean bottom seismometers are powerful tools for investigating the detailed structure of the oceanic mantle. However, most ocean bottom seismometers are placed above the low‐velocity water‐saturated sediment layer, such that their seismic records are inevitably affected by this seafloor sediment layer. Detailed constraints on the structure of the seafloor sediment layer allow us to accurately predict its effects on the observed seismic records. The seafloor sediment layer may consist of multiple discontinuities and vary regionally. This study proposes a new approach for characterizing the seismic properties of the seafloor sediment layer using teleseismic body waves recorded at ocean bottom seismometers. Synthetic tests show that the method can resolve thin sublayers within the sediment layer, such as a very thin low shear‐wave velocity layer and sharp seismic velocity jumps. Real data applications show that the sediment models obtained using this approach are in good agreement with those obtained from direct measurements and previous body‐wave analyses. Our proposed method will enable more detailed prediction and correction of the influence of the sediment layer on ocean bottom seismometer records, thereby providing more accurate constraints on deeper Earth structures. Key Points: The seismic properties of the seafloor sediment layer are characterized using ocean bottom seismometer teleseismic body‐wave recordsThe P‐ and S‐wave impedances and travel times and the VP/VS values of each sublayer within the sediment layer are resolvedIn situ seafloor sediment structure constraints will enable accurate correction and prediction of shallow structure effects [ABSTRACT FROM AUTHOR]

The NW Himalayan "seismic gap" lies between the 1905 and 2005 earthquake rupture zones and straddles the meisoseismal zone of the 1555‐Kashmir earthquake. Sufficient elastic strain has now accumulated across this region to drive a mega‐thrust earthquake. We model the crustal structure and geometry of the Main Himalayan Thrust (MHT) using Jammu and Kashmir Seismological NETwork data. P‐wave receiver‐function spatial and common‐conversion‐point (CCP) stacks, and joint inversion with Rayleigh‐wave group‐velocities are used to construct 3D Vs model of the region. This model reveals increasing crustal thickness from foreland‐to‐hinterland, and an under‐thrust Indian crust beneath NW Himalaya. The Moho is at ∼45 km depth beneath Sub‐Himalaya deepening to ∼70 km beneath Higher‐to‐Tethyan Himalaya with a gentle NE dip. The MHT forms a low‐velocity layer and has a flat–ramp geometry. The flat segment is beneath Sub‐to‐Lesser Himalaya at 6–10 km depth, and dips ∼4°. The mid‐crustal (frontal) ramp is beneath Kishtwar Higher‐Himalaya and Zanskar Range at 10–16 km depth, and dips ∼13–17°. Crustal structure varies along‐arc between east (Kishtwar) and west (Kashmir Valley) segments. A Lesser Himalayan duplex (LHD) lies beneath the Kishtwar Window and is bound by MHT and Main Central Thrust. The LHD high‐angle imbricate structures are illuminated by moderate seismicity. Beneath Kashmir Valley, the underthrust crust is ∼10 km thicker, has higher crustal Vs, and a shallower MHT at ∼10 km depth. The westward shallowing of MHT occurs through a lateral ramp. Aftershocks of the 2013‐Kishtwar earthquake concentrate on the frontal–lateral ramp intersection and mark the MHT down‐dip locked‐to‐creep transition. Plain Language Summary: We model the 3D‐seismic‐velocity structure of the NW Himalaya using data from the Jammu and Kashmir Seismological NETwork. The network extends from the Sub‐Himalaya to Tethyan Himalaya, across Himalayan thrust‐systems and rock units. We use distant earthquake body‐wave conversions and reverberations within the crust to construct 2D profiles, and perform joint modeling with surface‐wave dispersion data to compute 3D velocity model. Our results reveal under‐thrust Indian crust beneath NW Himalaya. The Moho at the base of the Indian crust is a boundary with increasing velocity as a function of depth, and deepens from Indo‐gangetic plains (∼45 km) to the Himalayas (∼70 km). The Main Himalayan Thurst (MHT), between the top of the under‐thrust Indian crust and overriding Himalayan wedge, is a low velocity layer with decreasing velocity as a function of depth. The MHT has flat‐ramp geometry beneath Kishtwar Himalaya, with ∼4° dipping flat and ∼13–17° dipping mid‐crustal (frontal) ramp. High‐angle imbricate thrusts overlies the MHT beneath Kishtwar Window and is illuminated by moderate earthquakes. Along‐arc the crust thickens by ∼10 km to the west beneath Kashmir Valley and MHT shallows through a SE‐dipping lateral ramp. Aftershocks of the 2013 Kishtwar earthquake concentrate on MHT frontal and lateral ramp intersection, at the down‐dip locked‐to‐creep transition. Key Points: Indian crust underthrusts the NW Himalaya with a gentle NE dip. Moho deepens from ∼45 to ∼75 km with undulations and southward dipMain Himalayan Thrust beneath Kishtwar has flat‐ramp geometry, overlain by Lesser Himalayan Duplex illuminated by moderate seismicityKashmir Valley has thicker crust, higher Vs, shallow MHT. MHT has SE dipping lateral ramp. Frontal‐lateral ramp intersection is seismogenic [ABSTRACT FROM AUTHOR]

The article focuses on the Special Issue on Inverse Problems in Acoustics, which highlights the increasing attention on solving problems related to sound source localization and sound field reconstruction through indirect measurements. Topics covered include techniques for capturing sound field information, inversion algorithms, and addressing challenges such as ill-posedness and instability.

The vast majority of pulsar wind nebulae (PWNe) present in the Galaxy is formed by middle-aged systems characterized by a strong interaction of the PWN itself with the supernova remnant (SNR). Unfortunately, modelling these systems can be quite complex and numerically expensive, due to the non-linearity of the PWN–SNR evolution even in the simple one-dimensional (1D)/one-zone case when the reverse shock of the SNR reaches the PWN, and the two begin to interact (and reverberation starts). Here, we introduce a new numerical technique that couples the numerical efficiency of the one-zone thin shell approach with the reliability of a full 'Lagrangian' evolution, able to correctly reproduce the PWN–SNR interaction during the reverberation, and to consistently evolve the particle spectrum beyond. Based on our previous findings, we show that our novel strategy resolves many of the uncertainties present in previous approaches, as the arbitrariness in the SNR structure, and ensure a robust evolution, compatible with results that can be obtained with more complex 1D dynamical approaches. Our approach enable us for the first time to provide reliable spectral models of the later compression phases in the evolution of PWNe. While in general, we found that the compression is less extreme than that obtained without such detailed dynamical considerations, leading to the formation of less structured spectral energy distributions, we still find that a non-negligible fraction of PWNe might experience a super-efficient phase, with the optical and/or X-ray luminosity exceeding the spin-down one. [ABSTRACT FROM AUTHOR]

Abstract—Narrowband spectra of sound scattered on the surface wave in the frequency range from 500 to 3000 Hz have been analyzed. Experimental results and theoretical models are reviewed. Previously published work by the authors is reviewed and new results are presented. The first characteristic case considered is forward scattering, where the sound transmitter and receiver are substantially separated from each other in space, and a continuous emission of a sinusoidal signal is produced. For this case, it is shown that the modulation spectrum of the scattered signal repeats the frequency spectrum of the surface wave with a certain coefficient and small corrections. The second considered characteristic case is a monostatic location, where the receiver and transmitter are combined and tone-pulse signals are emitted. Previously, for this case, it was implicitly expected that the reverberation spectrum would be generated by Bragg scattering on surface waves corresponding to half of the sound wavelength, and hence the spectrum of the scattered signal would be discrete. However, the experimental results indicate that the monostatic scattering spectra have a smooth bell-shape. Explaining this requires taking the effects of modulation of short surface waves by the long-wave component into account. Additionally, to explain the experimental phenomenon, the authors include a model of sound scattering on air bubbles, which are located in the near-surface layer of water and make oscillatory movements in the field of orbital currents of surface waves. [ABSTRACT FROM AUTHOR]

Haze weather can lead to reduced visibility of captured images, affecting daily production and life. In this paper, a new defogging technique for multi-exposure images combined with prior algorithm is proposed. Firstly, the transmittance of different regions of the haze image is calculated to obtain more accurate prior information. Secondly, gamma correction is applied to the prior map to obtain a set of multiple underexposure images. Thirdly, for the difference between global features and local details in image defogging, the multiple underexposure image set is decomposed into base and detail layers using guided filtering, and the fusion weight maps of the base layers image patches and the detail layers Laplacian decomposition are constructed, respectively. Finally, the haze-free image is reconstructed and restored. The haze image is selected from a standard dataset with different haze concentrations and compared with the commonly used haze removal algorithms. The defogging effect of this algorithm has better performance in visual effect and objective evaluation index. [ABSTRACT FROM AUTHOR]

Smooth interactions between talkers can be realized by transmitting and receiving mutual video images and voices in remote communication. However, in such remote communication, it is difficult to generate a sense of unity and an interactive atmosphere because humans recognize screens as a boundary of the physical space. Therefore, it is essential to develop a communication system that can generate and share an interactive atmosphere and interaction-activated communication even if talkers are in remote places. In this study, we developed a speech-driven avatar robot system incorporating an estimation model that simulates the degree of activated communication based on the talker's speech. The developed avatar robot system can visualize an interactive atmosphere while changing the complexion based on an estimated value. The effectiveness of the developed system was demonstrated by means of sensory evaluations. [ABSTRACT FROM AUTHOR]

Lightning is a ubiquitous source of infrasound. To study lightning flashes, thunder measurement efficiently complements electromagnetic observation. Using acoustical arrays, time delays between sensors inform on the direction of sound arrival, while the difference between emission time and sound arrival provides the source distance. Combining the two allows a geometrical reconstruction of individual lightning flashes, each viewed as a set of sound point sources. The measured sound amplitude can also be back‐propagated, compensating for absorption and density stratification. This allows to evaluate the acoustical power of each detected source, and the total power of an individual flash. This methodology is carried out to analyze data from two campaigns in Southern continental France (HyMeX‐SOP1, 2012) and in Corsica (EXAEDRE, 2018). Acoustic reconstruction is compared with ground and altitude localizations provided respectively by electromagnetic low frequency range Lightning Location Systems (LLS), and very high frequency range Lightning Mapping Array (LMA). In Corsica, power from reconstructed sources is also forward‐propagated toward several isolated microphones, and compared to measured signal, giving an additional validation of the power evaluation. Seventy eight events from the two campaigns are analyzed, including negative and positive cloud‐to‐ground discharges and intracloud ones. The analysis outlines the method efficiency and the strong variability of lightning as sound sources in terms of both power spatial distribution and overall value. Lastly, the correlation of this later with electrical parameters is investigated, either peak current (provided by LLS) or Charge Moment Change, resulting from broadband Extremely Low Frequency (ELF) measurements. Plain Language Summary: Lightning is a very powerful event generated by the electric activity inside the clouds. The discharges can occur either inside thunder clouds or connect cloud to ground. The energy generated by a flash is quite difficult to measure or estimate, and its value is still very discussed. Most of the estimations are made by analyzing the optical and electric emissions of the flashes, and it appears that it spreads among a wide range of values. As lightning flashes heat up their surrounding air very quickly, they generate a sound that can be detected a few dozen kilometers around. This thunder can be measured by several microphones and processed numerically to reconstruct the 3D shape of the lightning that caused it. The method we use allows to obtain the total power of each detected flash, which seems also very variable. This method also describes the spatial distribution of the power inside each flash. Surprisingly, in most cases, the thunder is not emitted with the same intensity at all by every portions of the lightning. On the contrary, there can be a high variability of the sound power of the different parts of the same lighting flash. Key Points: Localization and quantification of the acoustical power of lightning flashes along the lightning channel in 3DStatistics of acoustical power and comparison between different storms and flash polarityComparison between acoustical and electromagnetic parameters [ABSTRACT FROM AUTHOR]

To understand Herbert's conceptualization of ecology, I first connect his research and notes on I Dune i to the science of sand dune stabilization in coastal Oregon and, further, to the socio-economic and environmental crisis of the 1930s Dust Bowl. Making this argument for Herbert as green capitalist troubles contemporary conceptions of the text and author as environmentalist, but it is not, I argue, something with which Herbert would have disagreed.[14] By the 1970s, Herbert's conception of ecology was tied up with his own environmentalism. Just as I argue that the texts and research Herbert used shaped his understanding of ecology, my own sampling of his work and materials which populate this essay form a unique conception of Herbert's ecology in the late 1950s. Sand Dune Stabilization and Frank Herbert's Ecology The article that Herbert set out to write in 1957 was never published and never finished.[4] His editor, Lurton Blessingame, didn't think his ideas were clearly outlined and thought that the topic would have too little appeal, so Herbert never got past the proposal ("Shifting Sand Dunes"). [Extracted from the article]

Automatic speaker recognition has garnered significant attention in research, displaying impressive performance in matched conditions where training and testing environments are similar. However, the system's efficacy diminishes considerably when confronted with mismatched conditions, such as noise and reverberation. The extraction of features plays a pivotal role in determining the speaker recognition system's overall performance. Gammatone Frequency Cepstral Coefficients (GFCCs) have emerged as a commonly employed method for feature extraction in this domain. GFCCs exhibit robustness in handling environmental variations, encompassing diverse speaking styles and languages. Nevertheless, they prove sensitive to background conditions, including noise and reverberation, leading to a significant decline in system performance. A novel "Entrocy" feature has been proposed in response to this challenge. Entrocy is the Fourier Transform of Entrocy and aims to estimate the variation of information (or entropy) within an audio segment over time. A composite feature vector is formed by combining the Entrocy feature with GFCCs. The performance of this proposed approach was meticulously assessed using the i-vector PLDA baseline speaker recognition systems. Notably, the Entrocy feature consistently outperforms the well-established GFCC features, exhibiting robustness in these challenging conditions. Experiments conducted on speaker verification in controlled environments reveal that the speaker verification system can deliver high performance if the reverberation time does not exceed 1.0 s. Moreover, the speech samples need to be longer than 5 s are. These results highlight the effectiveness of the proposed method in reducing the equal error rate and improving the detection error trade-off, ultimately enhancing the system's overall accuracy. [ABSTRACT FROM AUTHOR]

In indoor environments, reverberation can distort the signalseceived by active noise cancelation devices, posing a challenge to sound classification. Therefore, we combined three speech spectral features based on different frequency scales into a densely connected network (DenseNet) to accomplish sound classification with reverberation effects. We adopted the DenseNet structure to make the model lightweight A dataset was created based on experimental and simulation methods, andhe classification goal was to distinguish between music signals, song signals, and speech signals. Using this framework, effectivexperiments were conducted. It was shown that the classification accuracy of the approach based on DenseNet and fused features reached 95.90%, betterhan the results based on other convolutional neural networks (CNNs). The size of the optimized DenseNet model is only 3.09 MB, which is only 7.76% of the size before optimization. We migrated the model to the Android platform. The modified model can discriminate sound clips faster on Android thanhe network before the modification. This shows that the approach based on DenseNet and fused features can dealith sound classification tasks in different indoor scenes, and the lightweight model can be deployed on embedded devices. [ABSTRACT FROM AUTHOR]

We develop a random forest regressor (RFR) machine learning model to trace the coronal evolution in two highly variable active galactic nuclei (AGNs) IRAS 13224-3809 and 1H 0707-495 observed with XMM–Newton , by probing the X-ray reverberation features imprinted on their power spectral density (PSD) profiles. Simulated PSDs in the form of a power law, with a similar frequency range and bins to the observed data, are produced. Then, they are convolved with relativistic disc-response functions from a lamp-post source before being used to train and test the model to predict the coronal height. We remove some bins that are dominated by Poisson noise and find that the model can tolerate the frequency-bin removal up to ∼10 bins to maintain a prediction accuracy of R 2 > 0.9. The black hole mass and inclination should be fixed so that the accuracy in predicting the source height is still >0.9. The accuracy also increases with the reflection fraction. The corona heights for both AGN are then predicted using the RFR model developed from the simulated PSDs, whose frequency range and bins are specifically adjusted to match those from each individual observation. The model suggests that their corona varies between ∼5–18  r g, with R 2 > 0.9 for all observations. Such high accuracy can still be obtained if the difference between the true mass and the trained value is |${\lesssim}10\ \hbox{per cent}$|⁠. Finally, the model supports the height-changing corona under the light-bending scenario, where the height is correlated to source luminosity in both IRAS 13224-3809 and 1H 0707-495. [ABSTRACT FROM AUTHOR]

Virtual transmitting arrays have been demonstrated for acoustic/elastic wave focusing in water and solids, using a chaotic cavity and a small number of transducers in conjunction with time-reversal acoustics. However, virtual arrays for focusing acoustic waves in air have received relatively little attention. This paper presents a virtual transmitting array based on a time-reversal chaotic cavity for airborne acoustic wave focusing. The virtual transmitting array comprises a three-dimensional printed chaotic cavity and a baffled loudspeaker to focus a high-amplitude acoustic wave only at the desired location. The spatial-temporal focusing performance of the virtual array was evaluated via a two-dimensional raster scan depending on the cavity shape, which is a crucial factor in the performance of a virtual array. The virtual array presented in this paper can be applied to focusing acoustic waves in environments with low reverberation and high absorption, where the application of time-reversal acoustics is challenging. [ABSTRACT FROM AUTHOR]

This paper proposes a diffuseness estimation-based single-source time–frequency point (SSTP) detection method for multisource direction of arrival (DOA) estimation. According to the composition, time–frequency (TF) points are divided into three types: SSTP, multisource TF, and interference TF. SSTPs and multisource TF points are defined as weak interference time–frequency points (WITPs). An SSTP is a TF point consisting only of the direct component of one sound source, which is beneficial for DOA estimation. Therefore, multisource DOA estimation is transformed into single-source DOA estimation by SSTP detection. Diffuseness estimation is introduced for a sound field microphone array. WITPs are detected by a diffuseness estimation–based detection method. Phase similarity determination is adopted to identify SSTPs from detected WITPs. Multiple sound source localization is completed by searching peaks in the normalized histogram of DOA estimates corresponding to the detected SSTPs. Experiments demonstrate that the proposed method achieves the precise detection of SSTPs, and evaluations show that it has superior accuracy of multiple sound source counting and localization in reverberant and noisy environments. [ABSTRACT FROM AUTHOR]