Acoustic Room Impulse Responses Research Articles

Iterative Echo Labeling Algorithm With Convex Hull Expansion for Room Geometry Estimation

Estimating room geometry is an important problem in audio signal processing, with various applications such as dereverberation and spatial audio. Several methods have been proposed to localize the positions of the walls given the times of arrival (TOAs) from acoustic room impulse responses (RIRs). To localize a reflector, reflections from the same wall should be identified first. When multiple microphones are widely installed inside a room, however, it is hard to identify the reflections generated by the same walls, which leads to the echo labeling problem. In this paper, we propose an iterative echo labeling algorithm to solve the echo labeling problem. We construct a convex hull of ellipses with first-arriving reflections at the initial iteration and expand the convex hull by utilizing later reflections at subsequent iterations. While expanding the convex hull, we sequentially localize the walls by inspecting tangents of the convex hull. Finally, the iteration is terminated when the expanded convex hull of ellipses becomes a superset of a room. As a result, the proposed algorithm can solve the echo labeling problem without prior knowledge of the number of walls and high computational complexity.

GpuRIR: A python library for room impulse response simulation with GPU acceleration

The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed. In this paper, we present a new implementation that dramatically improves the computation speed of the ISM by using Graphic Processing Units (GPUs) to parallelize both the simulation of multiple RIRs and the computation of the images inside each RIR. Additional speedups were achieved by exploiting the mixed precision capabilities of the newer GPUs and by using lookup tables. We provide a Python library under GNU license that can be easily used without any knowledge about GPU programming and we show that it is about 100 times faster than other state of the art CPU libraries. It may become a powerful tool for many applications that need to perform a large number of acoustic simulations, such as training machine learning systems for audio signal processing, or for real-time room acoustics simulations for immersive multimedia systems, such as augmented or virtual reality.

Iterative echo labeling technique for room geometry estimation

Room geometry estimation using acoustic room impulse responses (RIRs) has been tackled in various ways. Most of them detect wall locations using the time of arrival (TOA) of echoes between multiple microphones and loudspeakers. To combine TOA information from multiple room impulse responses, echoes from the same walls should be collected and processed together. When multiple microphones are widely distributed in a room, however, it is hard to identify walls responsible for the generation of each echo. In this work, an iterative update algorithm is proposed for resolving the echo labeling problem with low computational complexity. Unlike conventional algorithms utilizing the rank property of Euclidean distance matrix, the proposed algorithm utilizes the property of convex hull formed by ellipses representing individual TOAs. The location of the reflectors are sequentially identified from common tangent lines constituting the convex hull, and the search result is iteratively updated by inspecting later echoes in time. In addition, a termination criterion is also developed in order to enable the geometry estimation without the prior knowledge of the number of the reflectors.

Combined Acoustic MIMO Channel Crosstalk Cancellation and Room Impulse Response Reshaping

Virtual 3-D sound can be easily delivered to a listener by binaural audio signals that are reproduced via headphones, which guarantees that only the correct signals reach the corresponding ears. Reproducing the binaural audio signal by two or more loudspeakers introduces the problems of crosstalk on the one hand, and, of reverberation on the other hand. In crosstalk cancellation, the audio signals are fed through a network of prefilters prior to loudspeaker reproduction to ensure that only the designated signal reaches the corresponding ear of the listener. Since room impulse responses are very sensitive to spatial mismatch, and since listeners might slightly move while listening, robust designs are needed. In this paper, we present a method that jointly handles the three problems of crosstalk, reverberation reduction, and spatial robustness with respect to varying listening positions for one or more binaural source signals and multiple listeners. The proposed method is based on a multichannel room impulse response reshaping approach by optimizing a -norm based criterion. Replacing the well-known least-squares technique by a -norm based method employing a large value for allows us to explicitly control the amount of crosstalk and to shape the remaining reverberation effects according to a desired decay.

Multichannel Eigenspace Beamforming in a Reverberant Noisy Environment With Multiple Interfering Speech Signals

In many practical environments we wish to extract several desired speech signals, which are contaminated by nonstationary and stationary interfering signals. The desired signals may also be subject to distortion imposed by the acoustic room impulse responses (RIRs). In this paper, a linearly constrained minimum variance (LCMV) beamformer is designed for extracting the desired signals from multimicrophone measurements. The beamformer satisfies two sets of linear constraints. One set is dedicated to maintaining the desired signals, while the other set is chosen to mitigate both the stationary and nonstationary interferences. Unlike classical beamformers, which approximate the RIRs as delay-only filters, we take into account the entire RIR [or its respective acoustic transfer function (ATF)]. The LCMV beamformer is then reformulated in a generalized sidelobe canceler (GSC) structure, consisting of a fixed beamformer (FBF), blocking matrix (BM), and adaptive noise canceler (ANC). It is shown that for spatially white noise field, the beamformer reduces to a FBF, satisfying the constraint sets, without power minimization. It is shown that the application of the adaptive ANC contributes to interference reduction, but only when the constraint sets are not completely satisfied. We show that relative transfer functions (RTFs), which relate the desired speech sources and the microphones, and a basis for the interference subspace suffice for constructing the beamformer. The RTFs are estimated by applying the generalized eigenvalue decomposition (GEVD) procedure to the power spectral density (PSD) matrices of the received signals and the stationary noise. A basis for the interference subspace is estimated by collecting eigenvectors, calculated in segments where nonstationary interfering sources are active and the desired sources are inactive. The rank of the basis is then reduced by the application of the orthogonal triangular decomposition (QRD). This procedure relaxes the common requirement for nonoverlapping activity periods of the interference sources. A comprehensive experimental study in both simulated and real environments demonstrates the performance of the proposed beamformer.

Bayesian regularization and nonnegative deconvolution for room impulse response estimation

This paper proposes Bayesian Regularization And Nonnegative Deconvolution (BRAND) for accurately and robustly estimating acoustic room impulse responses for applications such as time-delay estimation and echo cancellation. Similar to conventional deconvolution methods, BRAND estimates the coefficients of convolutive finite-impulse-response (FIR) filters using least-square optimization. However, BRAND exploits the nonnegative, sparse structure of acoustic room impulse responses with nonnegativity constraints and L/sub 1/-norm sparsity regularization on the filter coefficients. The optimization problem is modeled within the context of a probabilistic Bayesian framework, and expectation-maximization (EM) is used to derive efficient update rules for estimating the optimal regularization parameters. BRAND is demonstrated on two representative examples, subsample time-delay estimation in reverberant environments and acoustic echo cancellation. The results presented in this paper show the advantages of BRAND in high temporal resolution and robustness to ambient noise compared with other conventional techniques.

A fast exact FTF adaptive algorithm

In this paper we present a new method for the efficient implementation of the fast transversal filter(ftf) algorithm. Reduction of the arithmetic complexity is obtained by making use of the redundancy in the successive computations of the forward prediction error and the filtering error in the joint process. The resulting algorithm is exactly equivalent to the originalftf algorithm, hence retaining the same theoretical convergence characteristics and offering the least squares(ls) estimate at each recursion step without delay. Furthermore, the algorithm can be numerically stabilized by using a simple and effective stabilization measure which needs only one additional multiplication per recursion step. The equivalence of the proposed algorithm to the originalftf algorithm is demonstrated by simulations of an acoustic room impulse response identification.

Modified fast exact NLMS adaptive algorithm

A novel method for efficiently implementing the fast exact NLMS (FENLMS) algorithm is proposed. The method is based on partitioning the algorithm into an ‘updating’ part and a ‘fixed’ filtering part, leading to a uniform distribution and a significant reduction in the number of arithmetic operations within the block. Its application is illustrated on the basis of some simulation results dealing with the identification of an acoustic room impulse response.