Reverberant Environments Research Articles

Underwater Acoustic Localization Based on IR-GCC-PHAT in Reverberant Environments

This paper presents a method of underwater acoustic localization in reverberant environments. Time difference of arrival (TDOA) measurement algorithm is a key technology for estimating direction of arrival (DOA) of an underwater sound source. In strong multipath interference, the pseudo-peaks in a correlation function disturb the detection of a correct time position and lead to a large TDOA measurement error. The proposed algorithm computes a time difference by taking cross-correlation of two impulse responses and improves robustness to multipath interference. The comparison of TDOA algorithms is done by evaluating the position accuracy of underwater sound source in both simulation and experiment.

Deep ad-hoc beamforming

Far-field speech processing is an important and challenging problem. In this paper, we propose deep ad-hoc beamforming, a deep-learning-based multichannel speech enhancement framework based on ad-hoc microphone arrays, to address the problem. It contains three novel components. First, it combines ad-hoc microphone arrays with deep-learning-based multichannel speech enhancement, which reduces the probability of the occurrence of far-field acoustic environments significantly. Second, it groups the microphones around the speech source to a local microphone array by a supervised channel selection framework based on deep neural networks. Third, it develops a simple time synchronization framework to synchronize the channels that have different time delay. Besides the above novelties and advantages, the proposed model is also trained in single-channel fashion, so that it can easily employ new development of speech processing techniques. Its test stage is also flexible in incorporating any number of microphones without retraining or modifying the framework. We have developed many implementations of the proposed framework and conducted an extensive experiment in scenarios where the locations of the speech sources are far-field, random, and blind to the microphones. Results on speech enhancement tasks show that our method outperforms its counterpart that works with linear microphone arrays by a considerable margin in both diffuse noise reverberant environments and point source noise reverberant environments. We have also tested the framework with different handcrafted features. Results show that although good features lead to high performance, they do not affect the conclusion on the effectiveness of the proposed framework.

Enhanced target detection using a new combined sonar waveform design

In this paper, a method for combining wideband and narrowband waveforms is proposed to improve target detection in shallow water environments. In this regard, a sonar waveform design suitable for target speed has been proposed too. The method uses a wideband frequency-modulated waveform for high range resolution in a reverberation environment and the Doppler-sensitive (DS) waveform to improve the detection of moving targets. The proposed combined waveform is mathematically modeled, and the Calculating of ambiguity function (AF) is performed. The AF and autocorrelation function (ACF) of the designed waveform indicate both DS and fair range resolution features simultaneously exist. So the initial range resolution for target detection is preserved. The Peak to Sidelobe level (PSL) ratio of the proposed waveform is at least 25 dB more than the previous state of the art waveforms. Calculating the Q-function criterion for the designed waveform reveals the superior reverberation suppression and detection performance with respect to the state of the art waveforms. After calculating the reverberation channel model, waveform echo, and detection of the target, the probability of detection (Pd) versus signal-to-reverberation ratio (SRR) was simulated by the Monte Carlo method. It was shown that the proposed waveform improved the probability of target detection by 20 dB, comparing to the base waveforms.

A Biologically Inspired Sound Localisation System Using a Silicon Cochlea Pair

We present a biologically inspired sound localisation system for reverberant environments using the Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) cochlear model. The system exploits a CAR-FAC pair to pre-process binaural signals that travel through the inherent delay line of the cascade structures, as each filter acts as a delay unit. Following the filtering, each cochlear channel is cross-correlated with all the channels of the other cochlea using a quantised instantaneous correlation function to form a 2-D instantaneous correlation matrix (correlogram). The correlogram contains both interaural time difference and spectral information. The generated correlograms are analysed using a regression neural network for localisation. We investigate the effect of the CAR-FAC nonlinearity on the system performance by comparing it with a CAR only version. To verify that the CAR/CAR-FAC and the quantised instantaneous correlation provide a suitable basis with which to perform sound localisation tasks, a linear regression, an extreme learning machine, and a convolutional neural network are trained to learn the azimuthal angle of the sound source from the correlogram. The system is evaluated using speech data recorded in a reverberant environment. We compare the performance of the linear CAR and nonlinear CAR-FAC models with current sound localisation systems as well as with human performance.

Deep learning assisted sound source localization using two orthogonal first-order differential microphone arrays.

Sound source localization in noisy and reverberant rooms using microphone arrays remains a challenging task, especially for small-sized arrays. Recent years have seen promising advances on deep learning assisted approaches by reformulating the sound localization problem as a classification one. A key to the deep learning-based approaches lies in extracting sound location features effectively in noisy and reverberant conditions. The popularly adopted features are based on the well-established generalized cross correlation phase transform (GCC-PHAT), which is known to be helpful in combating room reverberation. However, the GCC-PHAT features may not be applicable to small-sized arrays. This paper proposes a deep learning assisted sound localization method using a small-sized microphone array constructed by two orthogonal first-order differential microphone arrays. An improved feature extraction scheme based on sound intensity estimation is also proposed by decoupling the correlation between sound pressure and particle velocity components in the whitening weighting construction to enhance the robustness of the time-frequency bin-wise sound intensity features. Simulation and real-world experimental results show that the proposed deep learning assisted approach can achieve higher spatial resolution and is superior to its state-of-the-art counterparts using the GCC-PHAT or sound intensity features for small-sized arrays in noisy and reverberant environments.

Generative adversarial networks for single channel separation of convolutive mixed speech signals

The suppression of interference for speech recognition is of great significance in noisy situation, especially in single channel receiving mode, the suppression of interference is much more difficult. In this paper, we propose a generative adversarial network (GAN) based method for single channel dereverberation and speech separation. Different from the existing methods, our method considers the influence of strong reverberation on the observed signals. The proposed network involves two parts: reverberation suppression and target speech enhancement. Firstly, we use an improved CyclyGAN to compensate the multi-path effect on both target speech and interference. Secondly, we propose a differentialGAN to extract both target speech and interference while the interference enhancement network can indirectly improve the performance of target speech enhancement network. We use the real and imaginary parts of the complex spectrum as the feature vector, which avoids the phase mismatch during signal recovery. Simulation results show that our method is superior to its competitors in terms of multiple metrics in severe reverberation environment.

Acoustic Source Localization in a Reverberant Environment Based on Sound Field Morphological Component Analysis and Alternating Direction Method of Multipliers

The acoustic imaging results in the diffuse field are seriously affected by reverberation. The acoustic source localization algorithms based on the free-field assumption will produce many artifacts in the reverberant environment. To achieve accurate acoustic source localization in a reverberant environment, a sound field morphological component analysis (SF-MCA) model and an enhanced alternating direction method of multipliers (ADMM) algorithm are proposed in this article. First, the solution of the inhomogeneous Helmholtz equation is analyzed to characterize the acoustic components. Second, Green’s function and plane-wave basis function serve as dictionaries for sparse representation of the acoustic signal in the frequency domain, and the corresponding decomposition coefficients are obtained by the enhanced ADMM algorithm. Finally, the accurate acoustic imaging results are realized in the reverberant chamber space with strong reverberation ( $T_{20}=174.30\,\,\text {ms}$ ). Experiments demonstrate the validation of the proposed SF-MCA method. The acoustic imaging obtained by the proposed SF-MAC dereverberation model is far superior to the acoustic imaging that treats the reverberation as the general Gaussian noise.

Separation of nonstationary sound fields based on the time-domain equivalent source method with single layer pressure-velocity measurements.

This paper proposes a sound field separation technique based on the time-domain equivalent source method with single layer pressure-velocity measurements to extract the nonstationary sound field radiated by the target source in a reverberant environment. This technique constructs a formulation that relates the pressures and particle velocities on a measurement surface to the strengths of time-domain equivalent sources arranged for modelling the outgoing and incoming waves. By solving the strengths of time-domain equivalent sources, the sounds coming from different sides of the measurement surface can be separated independently. In the proposed technique, the use of a time-domain equivalent source model allows the measurement surface to be arbitrarily shaped, thus providing the ability to analyze the arbitrarily shaped sources in a reverberant environment. Numerical simulations investigated the performance of the proposed technique when using different types of arrays, including planar, semi-cylindrical, and semi-spherical arrays, and an experiment with three loudspeakers located at two sides of the measurement surface was carried out to test the validity of the proposed technique. Both numerical and experimental results demonstrate that the proposed technique can remove the influence of disturbing sources in both time and space domains and separate out the target sound fields effectively.

Partially Matching Projection Decoding Method Evaluation Under Different Playback Conditions

The Ambisonic technique has been widely used in fields such as sound-field reproduction, sound source localization, and acoustic noise control. In this paper, a recently proposed Ambisonic decoding method, the partially matching projection method (PMPD), was described in detail and extended for more practical use with near-field sources and reverberant playback environments. Several experiments were conducted to evaluate the performance of the method. In the experiments, we compared performances of PMPD method with three other Ambisonic decoding methods, “All-round Ambisonic Decoding” (AllRAD) method, mode matching decoding (MMD) method and regularized mode matching decoding (RMMD) method. Different playback conditions were considered to investigate the generalization of the method, including unevenly and evenly distributed loudspeakers, as well as free-field and reverberant playback environments. In terms of reproduction errors near sweep-spot and energy of loudspeaker gains, PMPD method worked well for different signals and playback environments. Whereas the other three methods encontered ill-posed problems or large reproduction errors in certain conditions. This means that PMPD method achieves a balence between precise reproductioin near sweet-spot and energy of loudspeaker gains with ill-posed problems. PMPD method has a more robust performance than the other three compared methods for different signals and playback environments. And we conclude that PMPD method is effective to be used as an Ambisonic decoding method, especially for different playback environments.

Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation

We address a blind source separation (BSS) problem in a noisy reverberant environment in which the number of microphones $M$ is greater than the number of sources of interest, and the other noise components can be approximated as stationary and Gaussian distributed. Conventional BSS algorithms for the optimization of a multi-input multi-output convolutional beamformer have suffered from a huge computational cost when $M$ is large. We here propose a computationally efficient method that integrates a weighted prediction error (WPE) dereverberation method and a fast BSS method called independent vector extraction (IVE), which has been developed for less reverberant environments. We show that, given the power spectrum for each source, the optimization problem of the new method can be reduced to that of IVE by exploiting the stationary condition, which makes the optimization easy to handle and computationally efficient. An experiment of speech signal separation shows that, compared to a conventional method that integrates WPE and independent vector analysis, our proposed method achieves much faster convergence while maintaining its separation performance.

Cobalt crust recognition based on kernel Fisher discriminant analysis and genetic algorithm in reverberation environment

Recognition of substrates in cobalt crust mining areas can improve mining efficiency. Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area, a method based on multiple-feature sets is proposed. Features of the target echoes are extracted by linear prediction method and wavelet analysis methods, and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted. Meanwhile, the characteristic matrices of modulus maxima, sub-band energy and multi-resolution singular spectrum entropy are obtained, respectively. The resulting features are subsequently compressed by kernel Fisher discriminant analysis (KFDA), the output features are selected using genetic algorithm (GA) to obtain optimal feature subsets, and recognition results of classifier are chosen as genetic fitness function. The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent. The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.

Geometry Calibration for Acoustic Transceiver Networks Based on Network Newton Distributed Optimization

Geometry calibration for distributed acoustic sensor networks is becoming increasing popular in the signal processing community. In this paper, a distributed geometry calibration method based on network Newton distributed optimization is proposed for the acoustic transceiver networks where each node consists of a microphone array and a loudspeaker. After collecting the direction-of-arrival and time-difference-of-arrival measurements, a two-stage centralized cost function is formulated to estimate the geometrical configuration of networks, and the corresponding identifiability conditions are discussed. Next, to achieve the distributed calibration, a distributed cost function is established by splitting the centralized cost function into multiple local cost functions. Finally, the distributed geometry calibration is carried out by using network Newton distributed optimization. The proposed method can effectively estimate the geometry structure of acoustic transceiver networks in noisy and reverberant environments. Compared with the existing approaches, it implements the calibration process in a distributed manner, which requires only the local communication among nodes and does not need an external central processor. Experimental results show the validity of the proposed method.

Analytical Model for the Relation Between Signal Bandwidth and Spatial Resolution in Steered-Response Power Phase Transform (SRP-PHAT) Maps

An analysis of the relationship between the bandwidth of acoustic signals and the required resolution of steered-response power phase transform (SRP-PHAT) maps used for sound source localization is presented. This relationship does not rely on the far-field assumption, nor does it depend on any specific array topology. The proposed analysis considers the computation of a SRP map as a process of sampling a set of generalized cross-correlation (GCC) functions, each one corresponding to a different microphone pair. From this approach, we derive a rule that relates GCC bandwidth with inter-microphone distance, resolution of the SRP map, and the potential position of the sound source relative to the array position. This rule is a sufficient condition for an aliasing-free calculation of the specified SRP-PHAT map. Simulation results show that limiting the bandwidth of the GCC according to such rule leads to significant reductions in sound source localization errors when sources are not in the immediate vicinity of the microphone array. These error reductions are more relevant for coarser resolutions of the SRP map, and they happen in both anechoic and reverberant environments.

Semi-Supervised Source Localization in Reverberant Environments With Deep Generative Modeling

Localization in reverberant environments remains an open challenge. Recently, supervised learning approaches have demonstrated very promising results in addressing reverberation. However, even with large data volumes, the number of labels available for supervised learning in such environments is usually small. We propose to address this issue with a semi-supervised learning (SSL) approach, based on deep generative modeling. Our chosen deep generative model, the variational autoencoder (VAE), is trained to generate the phase of relative transfer functions (RTFs) between microphones. In parallel, a direction of arrival (DOA) classifier network based on RTF-phase is also trained. The joint generative and discriminative model, deemed VAE-SSL, is trained using labeled and unlabeled RTF-phase sequences. In learning to generate and classify the sequences, the VAE-SSL extracts the physical causes of the RTF-phase (i.e., source location) from distracting signal characteristics such as noise and speech activity. This facilitates effective end-to-end operation of the VAE-SSL, which requires minimal preprocessing of RTF-phase. VAE-SSL is compared with two signal processing-based approaches, steered response power with phase transform (SRP-PHAT) and MUltiple SIgnal Classification (MUSIC), as well as fully supervised CNNs. The approaches are compared using data from two real acoustic environments - one of which was recently obtained at Technical University of Denmark specifically for our study. We find that VAE-SSL can outperform the conventional approaches and the CNN in label-limited scenarios. Further, the trained VAE-SSL system can generate new RTF-phase samples which capture the physics of the acoustic environment. Thus, the generative modeling in VAE-SSL provides a means of interpreting the learned representations. To the best of our knowledge, this paper presents the first approach to modeling the physics of acoustic propagation using deep generative modeling.

Robust Indoor Localization in Reverberant Environment using Cross Correlation Functions as Time-of-Arrival Likelihood

Robust Indoor Localization in Reverberant Environment using Cross Correlation Functions as Time-of-Arrival Likelihood

Generation of Personal Sound Zones With Physical Meaningful Constraints and Conjugate Gradient Method

Personal sound zones provide users to experience independent listening and quiet areas in the same acoustic environment using multiple loudspeakers. The generalized eigenvalue decomposition (GEVD) has been proposed for sound zones generation, allowing user to control the trade-off between acoustic contrast and signal distortion by adjusting some parameters. Unfortunately, these parameters are not physically meaningful, and the user has to tune them for different source materials and acoustic environments. Moreover, performing a high dimensional GEVD is computational complex. In this article, we first propose various strategies to control the reproduced sound zones as precisely and accurately as possible by reformulating the problem using physically meaningful constraints using regularization approach. Then, a hybrid approach of combining the conjugate gradient method and GEVD is proposed to reduce the computational complexity and signal distortion when the subspace dimension is small. The proposed methods show precise control over the reproduced sound zone via extensive numerical simulations in reverberant environments for different physically meaningful constraints.

On the Design of Differential Kronecker Product Beamformers

In this paper, we present a generalized approach for differential microphone array (DMA) beamforming in the short-time Fourier transform (STFT) domain. We propose a multistage beamforming approach, which considers a Kronecker product (KP) decomposition of the global beamformer into two independent sub-beamformers. We derive differential KP beamformers according to different criteria and analyze their performances, which are tuned by three design parameters. These parameters allow a high beamforming design flexibility; in particular, non-differential or non-KP beamformers may be obtained as special cases. Depending on the selection of parameters, we demonstrate a preferable performance with the new approach with respect to the white noise gain and directivity factor measures. In addition, we consider the task of speech enhancement. We show that differential KP beamformers perform better than non-differential and non-KP beamformers in terms of the quality and intelligibility of their respective time-domain enhanced signals, particularly in moderately reverberant environments.

Microphone Array Speech Separation Algorithm Based on TC-ResNet

Traditional separation methods have limited ability to handle the speech separation problem in high reverberant and low signal-to-noise ratio (SNR) environments, and thus achieve unsatisfactory results. In this study, a convolutional neural network with temporal convolution and residual network (TC-ResNet) is proposed to realize speech separation in a complex acoustic environment. A simplified steered-response power phase transform, denoted as GSRP-PHAT, is employed to reduce the computational cost. The extracted features are reshaped to a special tensor as the system inputs and implements temporal convolution, which not only enlarges the receptive field of the convolution layer but also significantly reduces the network computational cost. Residual blocks are used to combine multiresolution features and accelerate the training procedure. A modified ideal ratio mask is applied as the training target. Simulation results demonstrate that the proposed microphone array speech separation algorithm based on TC-ResNet achieves a better performance in terms of distortion ratio, source-to-interference ratio, and short-time objective intelligibility in low SNR and high reverberant environments, particularly in untrained situations. This indicates that the proposed method has generalization to untrained conditions.

Sparse Representations With Legendre Kernels for DOA Estimation and Acoustic Source Separation

Recording multiple sound sources in a reverberant environment results in convolutive mixtures. Sound sources can be extracted from microphone array recordings of such mixtures using acoustic source separation techniques. Acoustic source separation using recordings obtained from rigid spherical microphone arrays (RSMA) benefit from the representation of sound fields as series of spherical harmonics. More specifically, RSMAs afford increased flexibility in acoustic beamforming and spatial filtering. We propose a data-driven DOA estimation and acoustic source separation method based on a dictionary-based sparse decomposition of sound fields. The proposed method involves identifying the time-frequency bins with contributions from a single source only and those with sensor noise or diffuse sound field components. The former set of bins is used in DOA estimation and beamforming in the sparse decomposition domain. The latter set is used to calculate the diffuse field covariance matrix used in Wiener post-filtering to improve the source separation performance further. We demonstrate the utility of the proposed method via extensive objective and subjective evaluations.

An Adaptive Method Based on Multiscale Dilated Convolutional Network for Binaural Speech Source Localization

Most binaural speech source localization models perform poorly in unprecedentedly noisy and reverberant situations. Here, this issue is approached by modelling a multiscale dilated convolutional neural network (CNN). The time-related crosscorrelation function (CCF) and energy-related interaural level differences (ILD) are preprocessed in separate branches of dilated convolutional network. The multiscale dilated CNN can encode discriminative representations for CCF and ILD, respectively. After encoding, the individual interaural representations are fused to map source direction. Furthermore, in order to improve the parameter adaptation, a novel semiadaptive entropy is proposed to train the network under directional constraints. Experimental results show the proposed method can adaptively locate speech sources in simulated noisy and reverberant environments.