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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]

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]

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 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]

Speech translation systems have become indispensable in facilitating seamless communication across language barriers. This paper presents a cascade speech translation system tailored specifically for translating speech from the Kazakh language to Russian. The system aims to enable effective cross-lingual communication between Kazakh and Russian speakers, addressing the unique challenges posed by these languages. To develop the cascade speech translation system, we first created a dedicated speech translation dataset ST-kk-ru based on the ISSAI Corpus. The ST-kk-ru dataset comprises a large collection of Kazakh speech recordings along with their corresponding Russian translations. The automatic speech recognition (ASR) module of the system utilizes deep learning techniques to convert spoken Kazakh input into text. The machine translation (MT) module employs state-of-the-art neural machine translation methods, leveraging the parallel Kazakh-Russian translations available in the dataset to generate accurate translations. By conducting extensive experiments and evaluations, we have thoroughly assessed the performance of the cascade speech translation system on the ST-kk-ru dataset. The outcomes of our evaluation highlight the effectiveness of incorporating additional datasets for both the ASR and MT modules. This augmentation leads to a significant improvement in the performance of the cascade speech translation system, increasing the BLEU score by approximately 2 points when translating from Kazakh to Russian. These findings underscore the importance of leveraging supplementary data to enhance the capabilities of speech translation systems. [ABSTRACT FROM AUTHOR]

The acoustic conditions of open-plan office spaces influence the well-being and productivity perceived by users. However, with an inadequate evaluation of the workspace, acoustic design in open-plan offices can be a factor that alters user performance. Such is the case in Mexico, where there are no adequate standards to evaluate specific acoustic conditions such as intelligibility. For this reason, this case study aims to evaluate different types of measurement methods for intelligibility. This study was carried out at a university in northern Mexico. The sound measurements were based on the Mexican standard for noise analysis and the ISO 3382-part 3 standards for acoustic measurements for open-plan offices. The psychoacoustic parameters evaluated were reverberation and intelligibility, using objective methods determined on S/N and subjective methods based on loss of consonant, where it was analyzed the distance between the sound source and zones classified by building design characteristics. The results indicated at which points the intelligibility effects increased. We also observed that reverberation remained stable in this office and that the subjective methods presented a larger measured sound effect than the objective methods. This finding establishes that subjective methods conform to Lognormal behavior, which is applicable to other linguistic elements describing speech behavior. [ABSTRACT FROM AUTHOR]

The detection performance of active sonar is often hindered by the presence of seabed reverberation in shallow water. Separating the reverberations from the target echo and noise in the received signal is a crucial challenge in the field of underwater acoustic signal processing. To address this issue, an improved Go-SOR decomposition method is proposed based on the subspace-orbit-randomized singular value decomposition (SOR-SVD). This method successfully extracts the low-rank structure with a certain striation pattern. The results demonstrate that the proposed algorithm outperforms both the original Go algorithm and the current state-of-the-art (SOTA) algorithm in terms of the definition index of the low-rank structure and computational efficiency. Based on the monostatic reverberation theory of the normal mode, it is established that the low-rank structure is consistent with the low-frequency reverberation interference striation. This study examines the interference characteristics of the low-rank structure in the experimental sea area and suggests that the interferences of the fifth and seventh modes mainly control the low-rank structure. The findings of this study can be applied to seafloor exploration, reverberation waveguide invariant (RWI) extraction, and data-driven reverberation suppression methods. [ABSTRACT FROM AUTHOR]

Due to the small sample size of underwater acoustic data and the strong noise interference caused by seabed reverberation, recognizing underwater targets in Side-Scan Sonar (SSS) images is challenging. Using a transfer-learning-based recognition method to train the backbone network on a large optical dataset (ImageNet) and fine-tuning the head network with a small SSS image dataset can improve the classification of sonar images. However, optical and sonar images have different statistical characteristics, directly affecting transfer-learning-based target recognition. In order to improve the accuracy of underwater sonar image classification, a style transformation method between optical and SSS images is proposed in this study. In the proposed method, objects with the SSS style were synthesized through content image feature extraction and image style transfer to reduce the variability of different data sources. A staged optimization strategy using multi-modal data effectively captures the anti-noise features of sonar images, providing a new learning method for transfer learning. The results of the classification experiment showed that the approach is more stable when using synthetic data and other multi-modal datasets, with an overall accuracy of 100%. [ABSTRACT FROM AUTHOR]

Equipment, sound sources, operators, microphone placement, calculation techniques, and thermal–humidity measurement conditions all have an impact on the measurement of impulse responses when several channels are present. However, the thermal–humidity variable, which is a significant component of these factors impacting the assessment of acoustic characteristics, is commonly overlooked in research. The effects of altering temperature, relative humidity, and air velocity on acoustic parameters are investigated in this paper through experimental activities carried out in an experimental room. The patterns of fluctuation of a range of room acoustic characteristics are examined, data are acquired, and statistical analyses based on R (language and environment for statistical computing and graphics) are generated in order to ascertain the relationship between the variation of acoustic parameters and the variation of thermo-hygrometric parameters. Finally, a statistical analysis reveals relationships between thermal and hygrometric variables and interior acoustic characteristics. [ABSTRACT FROM AUTHOR]

We propose a multi-threaded algorithm that can improve the performance of geometric acoustic (GA)-based sound propagation algorithms in mobile devices. In general, sound propagation algorithms require high computational cost because they perform based on ray tracing algorithms. For this reason, it is difficult to operate sound propagation algorithms in mobile environments. To solve this problem, we processed the early reflection and late reverberation steps in parallel and verified the performance in four scenes based on eight sound sources. The experimental results showed that the performance of the proposed method was on average 1.77 times better than that of the single-threaded method, demonstrating that our algorithm can improve the performance of mobile devices. [ABSTRACT FROM AUTHOR]

Changes in acoustic parameters measured in a room may depend on the location and orientation of the sound source and microphones or on the reverberation conditions of the room. As was found in the research presented in this publication, reverberation in a room is also influenced by thermo-hygrometric conditions. The article presents an experimental analysis involving the impact of temperature and relative air humidity in a room on reverberation time. Since it is very difficult to control the temperature and relative humidity in real conditions, the tests were carried out both in laboratory conditions and with the use of simulations. For this purpose, the results of the reverberation time measurements in the reverberation chamber for various thermo-hygrometric conditions were obtained. Then, the reverberation chamber was modeled in the ODEON Version 11.0 program, and after the validation of the model, a series of simulations were performed, demonstrating the changes in the reverberation time as a function of temperature and relative air humidity. The results are presented in both a two-dimensional and three-dimensional version, i.e., the dependence of the reverberation time as a function of two variables: air temperature and relative humidity. [ABSTRACT FROM AUTHOR]

Multiple sound source separation in a reverberant environment has become popular in recent years. To improve the quality of the separated signal in a reverberant environment, a separation method based on a DOA cue and a deep neural network (DNN) is proposed in this paper. Firstly, a pre-processing model based on non-negative matrix factorization (NMF) is utilized for recorded signal dereverberation, which makes source separation more efficient. Then, we propose a multi-source separation algorithm combining sparse and non-sparse component points recovery to obtain each sound source signal from the dereverberated signal. For sparse component points, the dominant sound source for each sparse component point is determined by a DOA cue. For non-sparse component points, a DNN is used to recover each sound source signal. Finally, the signals separated from the sparse and non-sparse component points are well matched by temporal correlation to obtain each sound source signal. Both objective and subjective evaluation results indicate that compared with the existing method, the proposed separation approach shows a better performance in the case of a high-reverberation environment. [ABSTRACT FROM AUTHOR]

Wind profiler radar (WPR) is used for all-weather atmospheric wind-field monitoring. However, the reliability of these observations reduces significantly when there is electromagnetic interference echo, generally caused by ground objects, birds, or rain. Therefore, to optimize the data reliability of WPR, we proposed a synthetic data quality control process. The process included the application of a minimum connection method, judgment rule, and median test optimization algorithm for optimizing clutter suppression, spectral peak symmetry detection, and radial speed, respectively. We collected the base data from a radiosonde and multiple radars and conducted an experiment using these data and algorithms. The results indicated that the quality control method: (1) had good adaptability to multiple WPRs both in clear sky and precipitation; (2) was useful for suppressing ground clutter and (3) was superior to those of the manufacturer as a whole. Thus, the data quality control method proposed in this study can improve the accuracy and reliability of WPR products and multiple types of WPR, even when they function under vastly different weather conditions. [ABSTRACT FROM AUTHOR]

The rainfall regime is changing in the Catalan territory, likely in most areas in the Mediterranean Basin. This variability, spatial and temporal, means that there may be periods of severe drought combined with periods of heavy rainfall and floods. In this way, the management of water resources is complicated and can produce a high impact on different social aspects. The high convective activity leads to investigating the relationship between the electric discharges and radar parameters (reflectivity, echo top, vertically integrated liquid, and accumulated rainfall). The correlation allows identifying some elements that may be significant in terms of changes in rainfall regimes. Besides, using several radar parameters apart from precipitation accumulation reveals interesting explicit patterns of the previously known. These patterns can help better understand the precipitation behavior and the changes associated with it. [ABSTRACT FROM AUTHOR]

This paper presents a study on the perceived importance of different acoustic parameters of Binaural Room Impulse Response (BRIR) rendering. A headphone-based listening test was conducted with twenty expert participants. Three BRIRs generated from simulations of three different rooms were convolved with a dry speech signal and used as reference audio samples. Four BRIR parameters, Initial Time Delay Gap (ITDG), Forward Early Reflections (FER), Reverse Early Reflections (RER) and Late Reverberation (LR) were systematically altered and convolved with a speech signal to generate the test conditions. A staircase method was used to obtain the threshold at which each BRIR parameter was perceived as different from the reference audio sample. The average perceived impact threshold of each parameter was then calculated across the twenty participants. Results show that RER removal and ITDG extension have a clear impact on the perceptual reverberation of speech audio. Subjects were less sensitive to FER removal. The effect of LR removal on perceptual reverberation is hard to distinguish. Therefore, RER and ITDG are of particular importance when designing artificial reverberation algorithms, whilst more research is needed to understand the perceptual contribution of LR. Minor changes in FER and LR are less significant. [ABSTRACT FROM AUTHOR]

Time Domain Room Acoustic Solver with a Fourth-Order Explicit FEM Using Modified Time Inte... This paper by Yoshida et al. [[7]] presents a proposal of a time domain room acoustic solver using a novel fourth-order accurate explicit time domain finite element method (TD-FEM). Satisfactory acoustics is crucial for the ability of spaces such as auditoriums and lecture rooms to perform their primary function. To predict the sound absorption coefficient of the MEMM, the capability of three conventional models and three machine learning (ML) models of the supervised learning method were studied for the development of the prediction model. Potential of Room Acoustic Solver with the Plane-Wave Enriched Finite Element Method A preliminary study on the partition of unity finite element method (PUFEM) as a room acoustic solver was presented by Okuzono et al. [[6]]. [Extracted from the article]

The generalized spectral subtraction algorithm (GBSS), which has extraordinary ability in background noise reduction, is historically one of the first approaches used for speech enhancement and dereverberation. However, the algorithm has not been applied to de-noise the room impulse response (RIR) to extend the reverberation decay range. The application of the GBSS algorithm in this study is stated as an optimization problem, that is, subtracting the noise level from the RIR while maintaining the signal quality. The optimization process conducted in the measurements of the RIRs with artificial noise and natural ambient noise aims to determine the optimal sets of factors to achieve the best noise reduction results regarding the largest dynamic range improvement. The optimal factors are set variables determined by the estimated SNRs of the RIRs filtered in the octave band. The acoustic parameters, the reverberation time (RT), and early decay time (EDT), and the dynamic range improvement of the energy decay curve were used as control measures and evaluation criteria to ensure the reliability of the algorithm. The de-noising results were compared with noise compensation methods. With the achieved optimal factors, the GBSS contributes to a significant effect in terms of dynamic range improvement and decreases the estimation errors in the RTs caused by noise levels. [ABSTRACT FROM AUTHOR]

This paper presents a crossed rib diffuser (CRD) as an effective tool for room acoustic control. We performed an experimental investigation of its effectiveness using a specimen manufactured for this trial. The CRD is constructed by overlapping two one-dimensional (1D) periodic rib diffusers with different specifications so that they are crossed at non-right angles. The CRD achieves a higher scattering coefficient than 1D periodic rib diffusers in a wide band while maintaining the simple and friendly design of 1D periodic rib diffusers applicable to various architectural spaces. Moreover, inserting an absorbing layer between upper and lower ribs of the CRD, (CRD-A) yields a high broadband absorption coefficient. We first evaluated the random-incidence scattering coefficient of CRD using a 1/5 scaled model in comparison with those of 1D periodic diffusers assessed with a numerical method. Then, absorption coefficients for the CRD and the CRD-A were measured using a reverberation room. Subsequently, an experiment on a small meeting room with a 1D periodic rib diffuser, the CRD and the CRD-A was conducted to present performance of the CRD in room acoustic control. Impulse response measurements and evaluations of reverberation parameters ( T 20 and E D T ) and speech clarity ( D 50 ) were conducted. Additionally, we present differences in structure of reflected sounds found for the flat wall, the CRD and the CRD-A visually using a four-channel sound field microphone. [ABSTRACT FROM AUTHOR]

Sound environments in large public buildings are likely to be different from those of performance spaces, as well as those not specifically designed for acoustic "performance", but where sounds still play an important role because of the function they can promote (or disrupt). The aim of this study was identifying common strategies and empirical approaches researchers have been implementing for these acoustically complex enclosures and to provide some methodological indications for future studies on the topic. Studies conducted in three building types for crowd transit, such as museums/exhibition spaces, shopping malls, and transportation hubs/stations, which were collecting data about either physical outcomes or individual responses for such sound environments, were selected. The Scopus databases were searched for peer-reviewed journal papers published in English without time limitations. An additional manual search was performed on the reference lists of the retrieved items. The general consideration on inclusion was to meet the requirement that the case belonged to the three building types, and then the specific inclusion criteria were: (1) including at least an objective acoustic measure of the space; or (2) including at least a subjective measure of the space. The search returned 1060 results; after removing duplicates, two authors screened titles and abstracts and selected 117 papers for further analysis. Twenty-six studies were eventually included. Due to the limited number of items and differences in measures across studies, a quantitative meta-analysis could not be performed, and a qualitative approach was adopted instead. The most commonly used objective measures were SPL, and more specifically often considered as LAeq, and T. The intervals across studies were currently of inconsistency, and the selection is recommended to take space scale factor into account. The used subjective measures can be classified into four categories as annoyance, affective quality, room-acoustic quality, and acoustic spatiality. Four basic perceptual assessments concerning dynamic contents are accordingly suggested as "annoying-not annoying", "crowded-uncrowded", "long-short (reverberation)", and "far away-nearby". The other descriptors can be project-specific. The methodologies involve measurement, questionnaire/interview, listening test, and software simulation. It is necessary for the former two to consider temporal and spatial features of such spaces, and the adoption of the latter two will lead to better understanding of users' exposure in such spaces, e.g., acoustic sequences and user amount. The outputs of investigations inform that background noise level, e.g., 90 dB in museum/exhibition spaces, and sound reverberation, e.g., 4.0 to 5.0 s in shopping malls and transportation hubs/station, are of fundamental importance to the design of such spaces. Sufficient acoustic comfort can be achieved with integrated design of indoor soundscape. [ABSTRACT FROM AUTHOR]

The measurement of reverberation time is an essential procedure for the characterization of the acoustic performance of rooms. The values returned by these measurements allow us to predict how the sound will be transformed by the walls and furnishings of the rooms. The measurement of the reverberation time is not an easy procedure to carry out and requires the use of a space in an exclusive way. In fact, it is necessary to use instruments that reproduce a sound source and instruments for recording the response of the space. In this work, an automatic procedure for estimating the reverberation time based on the use of artificial neural networks was developed. Previously selected sounds were played, and joint sound recordings were made. The recorded sounds were processed with the extraction of characteristics, then they were labeled by associating to each sound the value of the reverberation time in octave bands of that specific room. The obtained dataset was used as input for the training of an algorithm based on artificial neural networks. The results returned by the predictive model suggest using this methodology to estimate the reverberation time of any closed space, using simple audio recordings without having to perform standard measurements or calculate the integration explicitly. [ABSTRACT FROM AUTHOR]

Sound source localization has been increasingly used recently. Among the existing techniques of sound source localization, the steered response power–phase transform (SRP-PHAT) exhibits considerable advantages regarding anti-noise and anti-reverberation. When applied in real-time situations, however, the heavy computational load makes it impossible to localize the sound source in a reasonable time since SRP-PHAT employs a grid search scheme. To solve the problem, an improved procedure called ODB-SRP-PHAT, i.e., steered response power and phase transformation with an offline database (ODB), was proposed by the authors. The basic idea of ODB-SRP-PHAT is to determine the possible sound source positions using SRP-PHAT and density peak clustering before real-time localization and store the identified positions in an ODB. Then, at the online positioning stage, only the power values of the positions in the ODB will be calculated. When used in real-time monitoring, e.g., locating the speaker in a video conference, the computational load of ODB-SRP-PHAT is significantly smaller than that of SRP-PHAT. Simulations and experiments under a real environment verified the high localization accuracy with a small computational load of ODB-SRP-PHAT. In addition, the advantages of anti-noise and anti-reverberation remained. The suggested procedure displayed good applicability in a real environment. [ABSTRACT FROM AUTHOR]

The integration of virtual acoustic environments (VAEs) with functional near-infrared spectroscopy (fNIRS) offers novel avenues to investigate behavioral and neural processes of speech-in-noise (SIN) comprehension in complex auditory scenes. Particularly in children with hearing aids (HAs), the combined application might offer new insights into the neural mechanism of SIN perception in simulated real-life acoustic scenarios. Here, we present first pilot data from six children with normal hearing (NH) and three children with bilateral HAs to explore the potential applicability of this novel approach. Children with NH received a speech recognition benefit from low room reverberation and target-distractors' spatial separation, particularly when the pitch of the target and the distractors was similar. On the neural level, the left inferior frontal gyrus appeared to support SIN comprehension during effortful listening. Children with HAs showed decreased SIN perception across conditions. The VAE-fNIRS approach is critically compared to traditional SIN assessments. Although the current study shows that feasibility still needs to be improved, the combined application potentially offers a promising tool to investigate novel research questions in simulated real-life listening. Future modified VAE-fNIRS applications are warranted to replicate the current findings and to validate its application in research and clinical settings. [ABSTRACT FROM AUTHOR]

Wireless remote microphones (RMs) transmit the desired acoustic signal to the hearing aid (HA) and facilitate enhanced listening in challenging environments. Fitting and verification of RMs, and benchmarking the relative performance of different RM devices in varied acoustic environments are of significant interest to Audiologists and RM developers. This paper investigates the application of instrumental speech intelligibility and quality metrics for characterizing the RM performance in two acoustic environments with varying amounts of background noise and reverberation. In both environments, two head and torso simulators (HATS) were placed 2 m apart, where one HATS served as the talker and the other served as the listener. Four RM systems were interfaced separately with a HA programmed to match the prescriptive targets for the N4 standard audiogram and placed on the listener HATS. The HA output in varied acoustic conditions was recorded and analyzed offline through computational models predicting speech intelligibility and quality. Results showed performance differences among the four RMs in the presence of noise and/or reverberation, with one RM exhibiting significantly better performance. Clinical implications and applications of these results are discussed. [ABSTRACT FROM AUTHOR]

State-of-the-art stereo recording techniques using two microphones have two main disadvantages: first, a limited reduction of the reverberation in the direct sound component, and second, compression or expansion of the angular position of sound sources. To address these disadvantages, the aim of this study is the development of a true stereo recording microphone array that aims to record the direct and reverberant sound field separately. This array can be used within the recording and playback configuration developed in Grosse and van de Par, 2015. Instead of using only two microphones, the proposed method combines two logarithmically-spaced microphone arrays, whose directivity patterns are optimized with a superdirective beamforming algorithm. The optimization allows us to have a better control of the overall beam pattern and of interchannel level differences. A comparison between the newly-proposed system and existing microphone techniques shows a lower percentage of the recorded reverberance within the sound field. [ABSTRACT FROM AUTHOR]

This paper discusses the modeling of the late part of a room impulse response by dividing it into short segments and approximating each one as a filtered random sequence. The filters and their associated gain account for the spectral shape and decay of the overall response. The noise segments are realized with velvet noise, which is sparse pseudo-random noise. The proposed approach leads to a parametric representation and computationally efficient artificial reverberation, since convolution with velvet noise reduces to a multiplication-free sparse sum. Cascading of the differential coloration filters is proposed to further reduce the computational cost. A subjective test shows that the resulting approximation of the late reverberation often leads to a noticeable difference in comparison to the original impulse response, especially with transient sounds, but the difference is minor. The proposed method is very efficient in terms of real-time computational cost and memory storage. The proposed method will be useful for spatial audio applications. [ABSTRACT FROM AUTHOR]

In recent years, many open-plan houses have been proposed not only for comfort reasons, but also as a place to engage in family life. However, in contrast to the fact that this kind of plan makes it easy for people to interact, the daily life household sounds that occur inside the home may be perceived as noise. It is especially difficult to suppress the propagation of sound and reverberation in staircase and stairwell areas due to the absence of sound-absorbing furniture. In this study, we focused on addressing sound management within the staircase area in open-plan housing where sound absorption is particularly difficult. In order to suppress sound propagation on the upper and lower floors and the reverberation of sound, we placed a thin sound absorption panel on the wall, ceiling, and riser of the staircase. As a result, we were able to confirm that the propagation of sound on upper and lower floors can be suppressed by carrying out the sound absorption treatment on the staircase. Furthermore, we found that in stairway landing areas, the suppression effect of the propagation of sound varies depending on the position of the sound source and the positioning of the sound absorption panel, and that there is a position for placing the sound absorption panel where the sound-absorbing effect is effective. [ABSTRACT FROM AUTHOR]

Audio feedback is defined as a positive feedback of acoustic signals where an audio input and output form a loop, and may be utilized artistically. This article presents new context-based controls over audio feedback, leading to the generation of desired sonic behaviors by enriching the influence of existing acoustic information such as room response and ambient noise. This ecological approach to audio feedback emphasizes mutual sonic interaction between signal processing and the acoustic environment. Mappings from analyses of the received signal to signal-processing parameters are designed to emphasize this specificity as an aesthetic goal. Our feedback system presents four types of mappings: approximate analyses of room reverberation to tempo-scale characteristics, ambient noise to amplitude and two different approximations of resonances to timbre. These mappings are validated computationally and evaluated experimentally in different acoustic conditions. [ABSTRACT FROM AUTHOR]

The Ando-Beranek's model, a linear version of Ando's subjective preference theory, obtained by the authors in a recent work, was combined with Barron revised theory. An optimal volume region for each reverberation time was obtained for classical music in symphony orchestra concert halls. The obtained relation was tested with good agreement with the top rated halls reported by Beranek and other halls with reported anomalies. [ABSTRACT FROM AUTHOR]

Due to the strong speckle noise caused by the seabed reverberation which makes it difficult to extract discriminating and noiseless features of a target, recognition and classification of underwater targets using side-scan sonar (SSS) images is a big challenge. Moreover, unlike classification of optical images which can use a large dataset to train the classifier, classification of SSS images usually has to exploit a very small dataset for training, which may cause classifier overfitting. Compared with traditional feature extraction methods using descriptors—such as Haar, SIFT, and LBP—deep learning-based methods are more powerful in capturing discriminating features. After training on a large optical dataset, e.g., ImageNet, direct fine-tuning method brings improvement to the sonar image classification using a small-size SSS image dataset. However, due to the different statistical characteristics between optical images and sonar images, transfer learning methods—e.g., fine-tuning—lack cross-domain adaptability, and therefore cannot achieve very satisfactory results. In this paper, a multi-domain collaborative transfer learning (MDCTL) method with multi-scale repeated attention mechanism (MSRAM) is proposed for improving the accuracy of underwater sonar image classification. In the MDCTL method, low-level characteristic similarity between SSS images and synthetic aperture radar (SAR) images, and high-level representation similarity between SSS images and optical images are used together to enhance the feature extraction ability of the deep learning model. Using different characteristics of multi-domain data to efficiently capture useful features for the sonar image classification, MDCTL offers a new way for transfer learning. MSRAM is used to effectively combine multi-scale features to make the proposed model pay more attention to the shape details of the target excluding the noise. Experimental results of classification show that, in using multi-domain data sets, the proposed method is more stable with an overall accuracy of 99.21%, bringing an improvement of 4.54% compared with the fine-tuned VGG19. Results given by diverse visualization methods also demonstrate that the method is more powerful in feature representation by using the MDCTL and MSRAM. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting by radar echo extrapolation using machine learning algorithms is a field worthy of further study, since rainfall prediction is essential in work and life. Current methods of predicting the radar echo images need further improvement in prediction accuracy as well as in presenting the predicted details of the radar echo images. In this paper, we propose a two-stage spatiotemporal context refinement network (2S-STRef) to predict future pixel-level radar echo maps (deterministic output) more accurately and with more distinct details. The first stage is an efficient and concise spatiotemporal prediction network, which uses the spatiotemporal RNN module embedded in an encoder and decoder structure to give a first-stage prediction. The second stage is a proposed detail refinement net, which can preserve the high-frequency detailed feature of the radar echo images by using the multi-scale feature extraction and fusion residual block. We used a real-world radar echo map dataset of South China to evaluate the proposed 2S-STRef model. The experiments showed that compared with the PredRNN++ and ConvLSTM method, our 2S-STRef model performs better on the precipitation nowcasting, as well as at the image quality evaluating index and the forecasting indices. At a given 45 dBZ echo threshold (heavy precipitation) and with a 2 h lead time, the widely used CSI, HSS, and SSIM indices of the proposed 2S-STRef model are found equal to 0.195, 0.312, and 0.665, respectively. In this case, the proposed model outperforms the OpticalFlow method and PredRNN++ model. [ABSTRACT FROM AUTHOR]

We observed a distinct drop-off region in the bearing-time record of acoustic reverberation data acquired from the south-western continental margin of the Ulleung Basin, East Sea, in the summer of 2015. 3 kHz continuous waves with pulse lengths of 0.1, 0.3, and 1.0 s were used as source pulses, with an R/V Cheonghae vessel towing a variable depth source and a triplet towed array toward the deep sea from shallow water. The observed pattern changed as the R/V Cheonghae moved across the continental slope further into the sea. This pattern arises as a result of the downward-refracted beams in the 1/2 convergence zone interacting with the soft bottom. In addition, the boundary of the drop-off region was modeled with the two-way maximum travel time of the first bottom-reflected rays using the bathymetry model of the General Bathymetric Chart of the Oceans, 2020. Some discrepancies were observed when comparing the modeled curve to the measured results, and the inaccuracy of the bathymetry model on the continental slope could be the main cause of these discrepancies. This pattern could be useful for bathymetry mapping, as well as estimations of source and receiver configurations. [ABSTRACT FROM AUTHOR]

Individual tree (IT) segmentation is crucial for forest management, supporting forest inventory, biomass monitoring or tree competition analysis. Light detection and ranging (LiDAR) is a prominent technology in this context, outperforming competing technologies. Aerial laser scanning (ALS) is frequently used for forest documentation, showing good point densities at the tree-top surface. Even though under-canopy data collection is possible with multi-echo ALS, the number of points for regions near the ground in leafy forests drops drastically, and, as a result, terrestrial laser scanners (TLS) may be required to obtain reliable information about tree trunks or under-growth features. In this work, an IT extraction method for terrestrial backpack LiDAR data is presented. The method is based on DBSCAN clustering and cylinder voxelization of the volume, showing a high detection rate (∼90%) for tree locations obtained from point clouds, and low commission and submission errors (accuracy over 93 % ). The method includes a sensibility assessment to calculate the optimal input parameters and adapt the workflow to real-world data. This approach shows that forest management can benefit from IT segmentation, using a handheld TLS to improve data collection productivity. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) provides high-resolution images of the Earth's surface irrespective of sunlight and weather conditions. In conventional spaceborne SAR, nadir echoes caused by the pulsed operation of SAR may significantly affect the SAR image quality. Therefore, the pulse repetition frequency (PRF) is constrained within the SAR system design to avoid the appearance of nadir echoes in the SAR image. As an alternative, the waveform-encoded SAR concept using a pulse-to-pulse variation of the transmitted waveform and dual-focus postprocessing can be exploited for nadir echo removal and to alleviate the PRF constraints. In particular, cyclically shifted chirps have been proposed as a possible waveform variation scheme. However, a large number of distinct waveforms is required to enable the simple implementation of the concept. This work proposes a technique based on the Eulerian circuit for generating a waveform sequence starting from a reduced number of distinct cyclically shifted chirps that can be effectively exploited for waveform-encoded SAR. The nadir echo suppression performance of the proposed scheme is analyzed through simulations using real TerraSAR-X data and a realistic nadir echo model that shows how the number of distinct waveforms and therefore the system complexity can be reduced without significant performance loss. These developments reduce the calibration burden and make the concept viable for implementation in future SAR systems. [ABSTRACT FROM AUTHOR]

With the development of remote sensing technology, the simultaneous acquisition of 3D point cloud and color information has become the constant goal for scientific research and commercial applications in this field. However, since radar echo data in practice refer to the value of the spectral channel and its corresponding energy, it is still impossible to obtain accurate tristimulus values of the point through color integral calculation after traditional normalization and multispectral correction. Furthermore, the reflectance of the target, the laser transmission power and other factors lead to the problems of no echo energy or weak echo energy in some bands of the visible spectrum, which further leads to large chromatic difference compared to the color calculated from the spectral reflectance of standard color card. In response to these problems, the hyperbolic tangent spectrum correction model with parameters is proposed for the spectrum correction of the acquired hyperspectral LiDAR in the 470–700 nm band. In addition, the improved gradient boosting decision tree sequence prediction algorithm is proposed for the reconstruction of missing spectrum in the 400–470 nm band where the echo energy is weak and missing. Experimental results show that there is relatively small chromatic difference between the obtained spectral information after correction and reconstruction and the spectrum of standard color card, achieving the purpose of true color reconstruction. [ABSTRACT FROM AUTHOR]

The performance of active sonar detection systems is seriously affected by the reverberation at the bottom of the waveguide in shallow water. In order to improve the performance of active sonar detection, it is necessary to understand the horizontal correlation of shallow-water bottom reverberation in active towed-array processing technology. However, the current research on the spatial correlation of reverberation is mainly based on vertical correlation, little work has been done on the horizontal correlation characteristics of long-distance seabed reverberation, and there is no support from sea test data. In this paper, the coupled mode reverberation model is applied to the horizontal correlation, and is studied according to the receiving position, time, and frequency. The simulation results show that, for the long-range bottom reverberation, the lateral correlation is greater than the longitudinal correlation in the horizontal space. By introducing the adiabatic mode solution, the mathematical model of horizontal correlation in the range-dependent waveguide with depth is derived. The numerical results show that the influence of the seabed dip angle on the horizontal correlation should be considered and that the horizontal correlation is affected obviously by the propagation effects of the sloped sea floor. Finally, the experimental data processing and analysis are given and verify the correctness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-channel cross-tower with attention mechanisms in latent domain network (Multi-TALK) that suppresses both the acoustic echo and background noise. The proposed approach consists of the cross-tower network, a parallel encoder with an auxiliary encoder, and a decoder. For the multi-channel processing, a parallel encoder is used to extract latent features of each microphone, and the latent features including the spatial information are compressed by a 1D convolution operation. In addition, the latent features of the far-end are extracted by the auxiliary encoder, and they are effectively provided to the cross-tower network by using the attention mechanism. The cross tower network iteratively estimates the latent features of acoustic echo and background noise in each tower. To improve the performance at each iteration, the outputs of each tower are transmitted as the input for the next iteration of the neighboring tower. Before passing through the decoder, to estimate the near-end speech, attention mechanisms are further applied to remove the estimated acoustic echo and background noise from the compressed mixture to prevent speech distortion by over-suppression. Compared to the conventional algorithms, the proposed algorithm effectively suppresses the acoustic echo and background noise and significantly lowers the speech distortion. [ABSTRACT FROM AUTHOR]

Currently, shore-based HF radars are widely used for coastal observations, and airborne radars are utilized for monitoring the ocean with a relatively large coverage offshore. In order to take the advantage of airborne radars, the theoretical mechanism of airborne HF/VHF radar for ocean surface observation has been studied in this paper. First, we describe the ocean surface wave height with the linear and nonlinear parts in a reasonable mathematical form and adopt the small perturbation method (SPM) to compute the HF/VHF radio scattered field induced by the sea surface. Second, the normalized radar cross section (NRCS) of the ocean surface is derived by tackling the field scattered from the random sea as a stochastic process. Third, the NRCS is simulated using the SPM under different sea states, at various radar operating frequencies and incident angles, and then the influences of these factors on radar sea echoes are investigated. At last, a comparison of NRCS using the SPM and the generalized function method (GFM) is done and analyzed. The mathematical model links the sea echoes and the ocean wave height spectrum, and it also offers a theoretical basis for designing a potential airborne HF/VHF radar for ocean surface remote sensing. [ABSTRACT FROM AUTHOR]

As acoustic waves are affected by the channel characteristics, such as scattering and reverberation when propagating in water, sonar images often exhibit speckle noise which will cause visual quality of the image to decrease. Therefore, denoising is a crucial preprocessing technique in sonar image applications. However, speckle noise is mainly caused by the sediment echo signals which are related to the background of seafloor sediment and can be obtained by prior modeling. Although deep learning-based denoising algorithms represent a research hotspot now, they are not suitable for such applications due to the high calculation amount and the large requirement of original images considering that sonar is carried by Autonomous Underwater Vehicles (AUVs) for collecting sonar images and performing calculation. In contrast, dictionary learning-based denoising method is more suitable and easier to be modeled. Compared with deep learning, it can greatly reduce the calculation amount and is more easily integrated into AUV systems. In addition, dictionary learning method based on image sparse representation can effectively achieve image denoising similarly. In order to solve the above problems, we propose a new adaptive dictionary learning method based on multi-resolution characteristics, which combines K-SVD dictionary learning with wavelet transform. Our method has the characteristics of dictionary learning and inherits the features of wavelet analysis as well. Compared with several classical methods, the proposed method is better at speckle noise reduction and edge detail preservation. At the same time, the calculation time is greatly reduced and the efficiency is significantly improved. [ABSTRACT FROM AUTHOR]

Shore-based phased-array HF radars have been widely used for remotely sensing ocean surface current, wave, and wind around the world. However, phase uncertainties, especially phase distortions, in receiving elements significantly degrade the performance of beam forming and direction-of-arrival (DOA) estimation for phased-array HF radar. To address this problem, the conventional array signal model is modified by adding a direction-based phase error matrix. Subsequently, an array phase manifold calibration method using antenna responses of incoming ship echoes is proposed. Later, an assessment on the proposed array calibration method is made based on the DOA estimations and current measurements that are obtained from the datasets that were collected with a multi-frequency HF (MHF) radar. MHF radar-estimated DOAs using three calibration strategies are compared with the ship directions that are provided by an Automatic Identification System (AIS). Additionally, comparisons between the MHF radar-derived currents while using three calibration strategies and Acoustic Doppler Current Profilers (ADCP)-measured currents are made. The results indicate that the proposed array calibration method is effective in DOA estimation and current measurement for phased-array HF radars, especially in the phase distortion situation. [ABSTRACT FROM AUTHOR]

This study addresses the thermal and energy performance assessment of a Building Integrated Photovoltaic Thermal (BIPVT) system installed on the façade of a test room in Solar XXI, a Net Zero Energy Building (NZEB) located in Lisbon, Portugal. A numerical analysis using the dynamic simulation tool EnergyPlus was carried out for assessing the performance of the test room with the BIPVT integrated on its façade through a parametric analysis of 14 scenarios in two conditions: a) receiving direct solar gains on the glazing surface and b) avoiding direct solar gains on the glazing surface. Additionally, a computational fluid dynamics (CFD) analysis of the BIPVT system was performed using ANSYS Fluent. The findings of this work demonstrate that the BIPVT has a good potential to improve the sustainability of the building by reducing the nominal energy needs to achieve thermal comfort, reducing up to 48% the total energy needs for heating and cooling compared to the base case. The operation mode must be adjusted to the other strategies already implemented in the room (e.g., the presence of windows and blinds to control direct solar gains), and the automatic operation mode has proven to have a better performance in the scope of this work. [ABSTRACT FROM AUTHOR]

Currently, several outdoor navigation and orientation electronic traveling aid (ETA) solutions for visually impaired (VI) people are commercially available or in active development. This paper's survey of blind experts has shown that after outdoor navigation, the second most important ETA feature for VI persons is indoor navigation and orientation (in public institutions, supermarkets, office buildings, homes, etc.). VI persons need ETA for orientation and navigation in unfamiliar indoor environments with embedded features for the detection and recognition of obstacles (not only on the ground but also at head level) and desired destinations such as rooms, staircases, and elevators. The development of such indoor navigation systems, which do not have Global Positioning System (GPS) locational references, is challenging and requires an overview and evaluation of existing systems with different navigation technologies. This paper presents an evaluation and comparison of state-of-the-art indoor navigation solutions, and the research implications provide a summary of the critical observations, some insights, and directions for further developments. The paper maps VI needs in relation to research and development (R&D) trends using the evaluation criteria deemed most important by blind experts. [ABSTRACT FROM AUTHOR]

To characterize the irregularity of the spectrum of a signal, spectral entropy and its variants are widely adopted measures. However, spectral entropy is invariant under the permutation of the power spectrum estimations on a predefined grid. This erases the inherent order structure in the spectrum. To disentangle the order structure and extract meaningful information from raw digital signal, a novel analysis method is necessary. In this paper, we tried to unfold this order structure by defining descriptors mapping real- and vector-valued power spectrum estimation of a signal into a scalar value. The proposed descriptors showed its potential in diverse problems. Significant differences were observed from brain signals and surface electromyography of different pathological/physiological states. Drastic change accompanied by the alteration of the underlying process of signals enables it as a candidate feature for seizure detection and endpoint detection in speech signal. Since the order structure in the spectrum of physiological signal carries previously ignored information, which cannot be properly extracted by existing techniques, this paper takes one step forward along this direction by proposing computationally efficient descriptors with guaranteed information gain. To the best of our knowledge, this is the first work revealing the effectiveness of the order structure in the spectrum in physiological signal processing. [ABSTRACT FROM AUTHOR]

This study presents the development and evaluation of a novel partially open-loop heat pump dryer with a unit-room (HPDU). The unit-room was designed to enable the ambient air to be mixed with the return air, thereby reducing the influence of the ambient air on the system performance, while maintaining a high system thermal efficiency. A modelling system for the HPDU was developed and validated based on a real-scale experimental study. By using the modelling system, the system characteristics under different ambient conditions and bypass factors were analyzed. The energy benefit of the proposed HPDU was quantified through a comparative study with a closed-loop heat pump dryer (CHPD). It is evident that a maximal specific moisture extraction rate (SMER) and a minimal total energy consumption (TEC) existed when changing the bypass factor of the HPDU under certain ambient temperatures. Compared to the CHPD, the coefficient of performance (COP) of the HPDU increased by up to 39.56%, presenting a significant energy benefit for the application of HPDU. [ABSTRACT FROM AUTHOR]