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Distortion of speech in real-life communication is inevitable, affecting its quality. Conventionally, the effectiveness of a speech system in terms of the perceptual quality of the speech it produces has been assessed using a time-consuming subjective metric, the mean opinion score. There are a number of objective metrics that can be used instead of the mean opinion score to assess the perceptual quality of the speech signal. The objective of this paper is to propose and validate a new objective metric, the spectral entropy-based metric (SEM), designed to evaluate the perceptual quality of speech and perceptual naturalness by quantifying spectral coherence. While other metrics focus on intelligibility, this study aims to fill a gap in naturalness assessment. The core novelty of this work lies in offering a diagnostic perspective on spectral coherence, an indicator of speech naturalness that is often not explicitly addressed by other metrics. To demonstrate the effectiveness of the proposed metric in evaluating the perceptual quality, we consider fixed-beam and steerable-beam first-order differential microphone arrays. Compared with other objective metrics, it is shown that the proposed SEM is more sensitive to spectral coherence, a predominant indicator of the naturalness of the output speech signal of a speech system.

Microphone array with minimum variance (MVDR) beamformer is a commonly used method for ambient noise suppression. Unfortunately, the performance of the MVDR beamformer is poor in a real reverberant room due to multipath wave propagation. To overcome this problem, we propose three improvements. Firstly, we propose end-fire microphone array that has been shown to have a better directivity index than the corresponding broadside microphone array. Secondly, we propose the use of unidirectional microphones instead of omnidirectional ones. Thirdly, we propose an adaptation of its adaptive algorithm during the pause of speech, which improves its robustness against the room reverberation and deviation from the optimal receiving direction. The performance of the proposed microphone array was theoretically analyzed using a diffuse noise model. Simulation analysis was performed for combined diffuse and coherent noise using the image model of the reverberant room. Real room tests were conducted using a four-microphone array placed in a small office room. The theoretical analysis and the real room tests showed that the proposed solution considerably improves speech quality.

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.

Microphone arrays methods are useful for determining the location and magnitude of rotating acoustic sources. This work presents an approach to calculating a discrete directivity pattern of a rotating sound source using inverse microphone array methods. The proposed method is divided into three consecutive steps. Firstly, a virtual rotating array method that compensates for motion of the source is employed in order to calculate the cross-spectral matrix. Secondly, the source locations are determined by a covariance matrix fitting approach. Finally, the sound source directivity is calculated using the inverse method SODIX on a reduced focus grid. Experimental validation and synthetic data from a simulation are used for the verification of the method. For this purpose, a rotating parametric loudspeaker array with a controllable steering pattern is designed. Five different directivity patterns of the rotating source are compared. The proposed method compensates for source motion and is able to reconstruct the location as well the directivity pattern of the rotating beam source.

<p>Previously, microphone arrays were used extensively used in beam-forming and estimation of source direction in speech enhancement problems. In Acoustic Array Systems: Theory, Implementation, and Application, the authors cover two other relatively less addressed problems: noise source identification and sound field visualization.&#160; Specifically, using these techniques, one is able to locate and even quantify noise sources.&#160; In addition, sound field distribution can be &#8220;visualized&#8221; by calculating the acoustical variables: pressure, particle velocity, and sound intensity. With comprehensive treatment of microphone arrays, the book covers an introduction to the theory, far-field and near-field array signal processing algorithms, practical implementations, and common applications, such as vehicles, computing and communications equipment, compressors, fans, and household appliances. The authors conclude with other emerging techniques and innovative algorithms.</p> <p>This book is ideal for postgraduates and researchers in acoustics, noise control engineering, audio engineering, and signal processing. It will also be helpful to practicing engineers in automotive, information, telecommunications, consumer electronics, cloud computing, and aerospace industries.<br /> <br /> </p> <ul> <li>Encompasses theory, implementation considerations and application know-how</li> <li>Provides theoretical background necessary for acoustic array systems</li> <li>Covers both farfield and nearfield techniques in a balanced way</li> <li>Introduces innovative algorithms including equivalent source imaging (NESI) and high-resolution nearfield arrays</li> <li>Selected code examples available for download for readers to practice on their own</li> <li>Presentation slides available for instructor use</li> </ul>

Ambient noise suppression in a reverberant room is usually performed by the microphone array. The adaptive beamforming, whose typical representative is minimum variance distortionless (MVDR) beamformer, is an effective method for noise suppression. However, MVDR beamformer gives poor results in the real room because of its sensitivity to the steering error and the multipath wave propagation. In this paper we propose a noise suppression method based on assumption that the positions of the speakers in the reverberant room are roughly known. Noise reduction is realized by two MVDR beamformers directed toward each of the speakers. Adaptation of the MVDR beamformers are controlled by a speaker activity detector which decision is based on power transfer model of the multiple superdirective beamformers in combined diffuse and coherent noise field. The proposed voice activity detector also provides residual noise reduction. The proposed method and its robustness to steering error were tested on the model of simulated room as well as in real room environment. The improvement of the restored speech signal was evaluated by Signal to Noise Ratio Enhancement (SNRE) and by Perceptual evaluation of speech quality (PESQ) measure.

This paper presents a novel approach for indoor acoustic source localization using microphone arrays, based on a Convolutional Neural Network (CNN). In the proposed solution, the CNN is designed to directly estimate the three-dimensional position of a single acoustic source using the raw audio signal as the input information and avoiding the use of hand-crafted audio features. Given the limited amount of available localization data, we propose, in this paper, a training strategy based on two steps. We first train our network using semi-synthetic data generated from close talk speech recordings. We simulate the time delays and distortion suffered in the signal that propagate from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results, evaluated on a publicly available dataset recorded in a real room, show that this approach is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies and also those presented in very recent proposals based on Convolutional Recurrent Neural Networks (CRNN). In addition, our experiments show that the performance of our CNN method does not show a relevant dependency on the speaker’s gender, nor on the size of the signal window being used.

This study is a survey of sound source localization and detection methods. The study provides a detailed classification of the methods used in the fields of science mentioned above. It classifies sound source localization systems based on criteria found in the literature. Moreover, an analysis of classic methods based on the propagation model and methods based on machine learning and deep learning techniques has been carried out. Attention has been paid to providing the most detailed information on the possibility of using physical phenomena, mathematical relationships, and artificial intelligence to determine sound source localization. Additionally, the article underscores the significance of these methods within both military and civil contexts. The study culminates with a discussion of forthcoming trends in the realms of acoustic detection and localization. The primary objective of this research is to serve as a valuable resource for selecting the most suitable approach within this domain.

Autonomous acoustic recorders are an increasingly popular method for low‐disturbance, large‐scale monitoring of sound‐producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time‐synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time‐synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor‐intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open‐source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low‐disturbance, large‐scale collection of animal position data. Acoustic localization can be used to locate a vocalizing animal spatially using an array of time‐synchronized microphones. We comprehensively review applications of acoustic localization for terrestrial wildlife. We describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization.

The purpose of this paper is to investigate the possibility of developing and using an intelligent, flexible, and reliable acoustic system, designed to discover, locate, and transmit the position of unmanned aerial vehicles (UAVs). Such an application is very useful for monitoring sensitive areas and land territories subject to privacy. The software functional components of the proposed detection and location algorithm were developed employing acoustic signal analysis and concurrent neural networks (CoNNs). An analysis of the detection and tracking performance for remotely piloted aircraft systems (RPASs), measured with a dedicated spiral microphone array with MEMS microphones, was also performed. The detection and tracking algorithms were implemented based on spectrograms decomposition and adaptive filters. In this research, spectrograms with Cohen class decomposition, log-Mel spectrograms, harmonic-percussive source separation and raw audio waveforms of the audio sample, collected from the spiral microphone array—as an input to the Concurrent Neural Networks were used, in order to determine and classify the number of detected drones in the perimeter of interest.