Steered Response Power Phase Transform Research Articles

Sound Source Localization Based on Multi-Channel Cross-Correlation Weighted Beamforming.

Beamforming and its applications in steered-response power (SRP) technology, such as steered-response power delay and sum (SRP-DAS) and steered-response power phase transform (SRP-PHAT), are widely used in sound source localization. However, their resolution and accuracy still need improvement. A novel beamforming method combining SRP and multi-channel cross-correlation coefficient (MCCC), SRP-MCCC, is proposed in this paper to improve the accuracy of direction of arrival (DOA). Directional weight (DW) is obtained by calculating the MCCC. Based on DW, suppressed the non-incoming wave direction and gained the incoming wave direction to improve the beamforming capabilities. Then, sound source localizations based on the dual linear array under different conditions were simulated. Compared with SRP-PHAT, SRP-MCCC has the advantages of high positioning accuracy, strong spatial directivity and robustness under the different signal–noise ratios (SNRs). When the SNR is −10 dB, the average positioning error of the single-frequency sound source at different coordinates decreases by 5.69%, and that of the mixed frequency sound sources at the same coordinate decreases by 5.77%. Finally, the experimental verification was carried out. The results show that the average error of SRP-MCCC has been reduced by 8.14% and the positioning accuracy has been significantly improved, which is consistent with the simulation results. This research provides a new idea for further engineering applications of sound source localization based on beamforming.

Acoustic Source Localization Using a Geometrically Sampled Grid SRP-PHAT Algorithm With Max-Pooling Operation

The steered response power phase transform (SRP-PHAT) is a well-known algorithm for acoustic source localization using microphone arrays. It consists in the computation of the generalized cross-correlation (GCC) between each microphone pair, and in the coherent summation of the GCC values in the grid search space. Several improvements based on the volumetric grid have been proposed in order to achieve spatial resolution scalability and to reduce the computational cost by using a coarser grid. In general, the problem of the volumetric based methods is that the noise and the reverberation are projected into the search space since all GCC information is used to build the acoustic map. It is hence proposed a volumetric grid SRP-PHAT algorithm based on the geometrically sampled grid (GSG) that incorporates a max-pooling (MP) operation in the volume accumulation of the GCC values in order to improve the localization performance. The MP is the solution of a minimization-maximization problem that aims at minimizing the deleterious effect of noise and reverberation and at maximizing the accuracy of the GCC values related to the target sound source. Simulations and real-world experiments demonstrate the efficiency of the proposed SRP-GSG-MP algorithm in adverse conditions.

Microphone Array-Based Sound Source Localization Using Convolutional Residual Network

Microphone array-based sound source localization (SSL) is widely used in a variety of occasions such as video conferencing, robotic hearing, speech enhancement, speech recognition and so on. The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments. In order to improve localization performance, a novel SSL algorithm using convolutional residual network (CRN) is proposed in this paper. The spatial features including time difference of arrivals (TDOAs) between microphone pairs and steered response power-phase transform (SRP-PHAT) spatial spectrum are extracted in each Gammatone sub-band. The spatial features of different sub-bands with a frame are combine into a feature matrix as the input of CRN. The proposed algorithm employ CRN to fuse the spatial features. Since the CRN introduces the residual structure on the basis of the convolutional network, it reduce the difficulty of training procedure and accelerate the convergence of the model. A CRN model is learned from the training data in various reverberation and noise environments to establish the mapping regularity between the input feature and the sound azimuth. Through simulation verification, compared with the methods using traditional deep neural network, the proposed algorithm can achieve a better localization performance in SSL task, and provide better generalization capacity to untrained noise and reverberation.

Iterative Volumetric Reduction (IVR) Steered Response Power Method for Acoustic Source Localization

Background: The Steered Response Power–Phase Transform (SRP-PHAT) approach is a breakthrough in the field of source localization in a high reverberant environment, but the substantial computational cost limits its usage in the practical implementation. Objective: This paper presents an efficient SRP-PHAT based, Iterative Volumetric Reduction (IVR) Steered Response Power method for acoustic source localization in non-ideal surroundings. Methods: Developing the coarse acoustic maps in the preliminary iterations provides a measure of the approximate likelihood of a source lying in the corresponding regions. Subsequently, the search space volume is reduced to the region containing points with significant likelihood. Further, these two steps are iterated to achieve the desired resolution. Results and Conclusion: Conclusively, the computational load reduces by 60-80% with considerable upgrading in accuracy as compared to Modified SRP PHAT. The required functional evaluation is adaptable to the environment, which makes it an excellent approach over Iterative modified SRP-PHAT in unfavourable circumstances. Experiments conducted in various scenarios demonstrate the method's significance over the other state–of-the-art methods.

Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering

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.

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.

Exploiting a geometrically sampled grid in the steered response power algorithm for localization improvement.

The steered response power phase transform (SRP-PHAT) is a beamformer method very attractive in acoustic localization applications due to its robustness in reverberant environments. This paper presents a spatial grid design procedure, called the geometrically sampled grid (GSG), which aims at computing the spatial grid by taking into account the discrete sampling of time difference of arrival (TDOA) functions and the desired spatial resolution. A SRP-PHAT localization algorithm based on the GSG method is also introduced. The proposed method exploits the intersections of the discrete hyperboloids representing the TDOA information domain of the sensor array, and projects the whole TDOA information on the space search grid. The GSG method thus allows one to design the sampled spatial grid which represents the best search grid for a given sensor array, it allows one to perform a sensitivity analysis of the array and to characterize its spatial localization accuracy, and it may assist the system designer in the reconfiguration of the array. Experimental results using both simulated data and real recordings show that the localization accuracy is substantially improved both for high and for low spatial resolution, and that it is closely related to the proposed power response sensitivity measure.

Parallel SRP-PHAT for GPUs

The steered response power phase transform (SRP-PHAT) is one of the widely used algorithms for sound source localization. Since it must examine a large number of candidate sound source locations, conventional SRP-PHAT approaches may not be used in real time. To overcome this problem, an effort was made previously to parallelize the SRP-PHAT on graphics processing units (GPUs). However, the full capacities of the GPU were not exploited since on-chip memory usage was not addressed. In this paper, we propose GPU-based parallel algorithms of the SRP-PHAT both in the frequency domain and time domain. The proposed methods optimize the memory access patterns of the SRP-PHAT and efficiently use the on-chip memory. As a result, the proposed methods demonstrate a speedup of 1276 times in the frequency domain and 80 times in the time domain compared to CPU-based algorithms, and 1.5 times in the frequency domain and 6 times in the time domain compared to conventional GPU-based methods.

Fast Sound Source Localization Using Two-Level Search Space Clustering.

Steered response power phase transform (SRP-PHAT) is a method that is widely used for robust sound source localization (SSL). However, since SRP-PHAT searches over a large number of candidate locations, it is too slow to run in real-time for large-scale microphone array systems. In this paper, we propose a robust two-level search space clustering method to speed-up SRP-PHAT-based SSL. The proposed method divides the candidate locations of the sound source into a set of groups and finds a small number of groups that are likely to contain the maximum power location. By searching within the small number of groups, the computational costs are reduced by 61.8% compared to a previously proposed method without loss of accuracy.

Robust and Fast Localization of Single Speech Source Using a Planar Array

Heavy computational load and acoustic interferences are two major problems to speech source localization in real applications. Conventional methods can mitigate one problem, but deteriorate the other. This letter proposes an algorithm of direction-of-arrival (DOA) estimation, which is both computationally efficient and robust in the presence of acoustic interferences. The robustness is considered in two aspects. One is the eigenanalysis-based enhancement to reduce acoustic interferences such as noise and reverberation. The other is the coefficients that weight the pairwise time delays to mitigate the effect of delay outliers on DOA. The high computational efficiency is achieved by making use of a concave cost function, from which, the optimal estimate of DOA is given by a closed-form solution. The grid-search method often adopted in conventional algorithms is no longer used in this algorithm. We conduct some experiments in both simulated and real environments with a 9-element circular array. The proposed algorithm runs about ten times faster than Steered Response Power PHAse Transform (SRP-PHAT), and outperforms SRP-PHAT in terms of robustness.

Accelerated steered response power method for sound source localization via clustering search

The steered response power-phase transform (SRP-PHAT) sound source localization algorithm is robust in a real environment. However, the large computation complexity limits the practical application of SRP-PHAT. For a microphone array, each location corresponds to a set of time differences of arrival (TDOAs), and this paper collects them into a TDOA vector. Since the TDOA vectors in the adjacent regions are similar, we present a fast algorithm based on clustering search to reduce the computation complexity of SRP-PHAT. In the training stage, the K-means or Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering algorithm is used to find the centroid in each cluster with similar TDOA vectors. In the procedure of sound localization, the optimal cluster is found by comparing the steered response powers (SRPs) of all centroids. The SRPs of all candidate locations in the optimal cluster are compared to localize the sound source. Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computational load of the proposed method with those of the conventional SRP-PHAT algorithm. The results show that the proposed method is able to reduce the computational load drastically and maintains almost the same localization accuracy and robustness as those of the conventional SRP-PHAT algorithm. The difference in localization performance brought by different clustering algorithms used in the training stage is trivial.

Full Azimuth Multiple Sound Source Localization with 3-Channel Microphone Array

This paper describes a method for estimating Direction Of Arrival (DOA) of multiple sound sources in full azimuth with three microphones. Estimating DOA with paired microphone arrays creates imaginary sound sources because of time delay of arrival (TDOA) being identical between real and imaginary sources. Imaginary sound sources can create chronic problems in multiple Sound Source Localization (SSL), because they can be localized as real sound sources. Our proposed approach is based on the observation that each microphone array creates imaginary sound sources, but the DOA of imaginary sources may be different depending on the orientation of the paired microphone array. With the fact that a real source would always be localized in the same direction regardless of the array orientation, we can suppress the imaginary sound sources by minimum filtering based on Steered Response Power — Phase Transform (SRP-PHAT) method. A set of experiments conducted in a real noisy environment showed that the proposed method was accurate in localizing multiple sound sources.

A Modified SRP-PHAT Functional for Robust Real-Time Sound Source Localization With Scalable Spatial Sampling

The Steered Response Power - Phase Transform (SRP-PHAT) algorithm has been shown to be one of the most robust sound source localization approaches operating in noisy and reverberant environments. However, its practical implementation is usually based on a costly fine grid-search procedure, making the computational cost of the method a real issue. In this letter, we introduce an effective strategy that extends the conventional SRP-PHAT functional with the aim of considering the volume surrounding the discrete locations of the spatial grid. As a result, the modified functional performs a full exploration of the sampled space rather than computing the SRP at discrete spatial positions, increasing its robustness and allowing for a coarser spatial grid. To this end, the Generalized Cross-Correlation (GCC) function corresponding to each microphone pair must be properly accumulated according to the defined microphone setup. Experiments carried out under different acoustic conditions confirm the validity of the proposed approach.

Sound source localization for robot auditory systems

Sound source localization (SSL) is a major function of robot auditory systems for intelligent home robots. The steered response power-phase transform (SRP-PHAT) is a widely used method for robust SSL. However, it is too slow to run in real time, since SRP-PHAT searches a large number of candidate sound source locations. This paper proposes a search space clustering method designed to speed up the SRP-PHAT based sound source localization algorithm for intelligent home robots equipped with small scale microphone arrays. The proposed method reduces the number of candidate sound source locations by 30.6% and achieves 46.7% error reduction compared to conventional methods.