Direct-to-reverberant Ratio Research Articles

Impact of direct-to-reverberation ratio and interaural cross-correlation on externalization in binaural audio rendering

To achieve realistic binaural reproduction of spatial audio in a headphone scenario, addressing inside-the-head-locatedness is crucial, which can be accomplished through appropriate externalization techniques. Two acoustic cues, the direct-to-reverberation ratio (DRR) and interaural cross-correlation (IACC), are known to influence perceived externalization. However, the precise relationship between these cues and their combined effect has not been extensively studied. In this investigation, we aim to explore the relative contribution of DRR and IACC to externalization by examining various combinations of these two acoustic cues. To this end, participants were exposed to various binaural room impulse responses (BRIRs) synthesized to produce three distinct levels of DRRs and IACCs for two different source directions. The listening test was conducted in an anechoic chamber with participants’ eyes blindfolded to minimize the influence of room divergence effect. We also investigated the influence of head tracking by conducting experiments with and without head trackers. The correlation maps between externalization and the two acoustic cues were generated for each source direction. The study reveals that the contribution of the two acoustic cues differs with and without head tracking, and the joint contribution also varies depending on the source direction and amount of reflections included in the BRIR.

Late Reverberant Spectral Variance Estimation for Single-Channel Dereverberation Using Adaptive Parameter Estimator

The estimation of the late reverberant spectral variance (LRSV) is of paramount importance in most reverberation suppression algorithms. This letter proposes an improved single-channel LRSV estimator based on Habets LRSV estimator by using an adaptive parameter estimator. Instead of estimating the direct-to-reverberation ratio (DRR), the proposed LRSV estimator directly estimates the parameter κ in a generalized statistical model since the experimental results show that even the κ calculated using measured ground truth DRR may not be the optimal parameter for the LRSV estimator. Experimental results using synthetic reverberant signals demonstrate the superiority of the proposed estimator to conventional approaches.

The Effect of Partial Time-Frequency Masking of the Direct Sound on the Perception of Reverberant Speech

The perception of sound in real-life acoustic environments, such as enclosed rooms or open spaces with reflective objects, is affected by reverberation. Hence, reverberation is extensively studied in the context of auditory perception, with many studies highlighting the importance of the direct sound for perception. Based on this insight, speech processing methods often use time-frequency (TF) analysis to detect TF bins that are dominated by the direct sound, and then use the detected bins to reproduce or enhance the speech signals. The detection of bins dominated by the direct sound is typically based on an objective measure, such as the direct-to-reverberant ratio (DRR). However, the relation between the DRR in the TF bins and the spatial perception of the reverberant sound which is reproduced from these bins is still not clear. It is the aim of this paper to provide some insights into this relation, specifically for reverberant speech, focusing on bins with high DRR. This is performed using a listening experiment, where high DRR bins within a reverberant speech signal have been masked in the TF domain, based on various DRR thresholds. The results show that the percentage of high-DRR TF bins that were masked may better indicate the quality of spatial perception, compared to the specific value of the DRR threshold. The insights from this work could be incorporated into spatial audio techniques that reproduce the direct sound of reverberant speech, and potentially improve spatial perception. This was illustrated with an implementation of directional audio coding that was studied with an additional listening experiment supporting the previously described results.

Blind estimation of the direct-to-reverberant ratio using a beta distribution fit to binaural coherence

Knowledge of the direct-to-reverberant ratio (DRR) between an acoustic source and a listening position or receiver can be useful for various acoustic and audio applications including dereverberation, source distance estimation, automatic speech recognition, and binaural synthesis. While the DRR can be computed easily from a room impulse response (RIR), blind estimation using acoustic sources of opportunity is necessary when such RIRs are not available. In this presentation, we describe an approach for blind estimation of the DRR which uses the magnitude-squared coherence (MSC) between the two channels of a binaural signal. The method involves fitting a beta distribution to the MSC values, aggregated over time and frequency, and deriving the estimate from the relationship between the DRR and the shape parameters of that distribution. Validation experiments utilizing speech convolved with binaural RIRs collected from a variety of publicly available datasets yield DRR estimates that are within the just-noticeable difference for DRRs in the range -15 to + 18 dB.

Blind estimation of the direct-to-reverberant ratio using a beta distribution fit to binaural coherence.

Knowledge of the direct-to-reverberant ratio (DRR) can be useful in various acoustic and audio applications. While the DRR can be computed easily from a room impulse response (RIR), blind estimation using sources of opportunity is necessary when such RIRs are not available. This paper describes a method for blind estimation of the DRR which involves fitting a beta distribution to the magnitude-squared coherence between two binaural audio signals, aggregated over time and frequency. Validation experiments utilizing speech convolved with binaural RIRs yield DRR estimates that are within the just-noticeable difference for DRRs in the range -15 to +18 dB.

The effect of simulated room acoustic parameters on the intelligibility and perceived reverberation of monosyllabic words and sentences.

Individual acoustic parameters of reverberation have the potential to affect both the intelligibility of speech and the degree of perceived reverberation. The current experiments used monaural acoustic simulations to investigate the effect of reverberation time (RT) and direct-to-reverberant ratio (DRR) on word and sentence intelligibility at different levels of analysis (phonemes, words, and sentences). Perceived reverberation and recall of sentences were also assessed. Intelligibility and perceived reverberation decreased with increasing RT and decreasing DRR (particularly between 0 and -10 dB). Results indicate consistent effects of both RT and DRR on the intelligibility and perceived reverberation of words and sentences.

The effect of pinna filtering in binaural transfer functions on externalization in a reverberant environment

The perception of the externalization of noise stimuli is investigated with respect to the pinna filtering effect on direct and reverberant parts of binaural transfer functions. Individual binaural room impulse responses (BRIRs) recorded with and without pinnae, called pinna BRIRs and head BRIRs respectively, are divided into the direct and reverberant parts. For frontal and lateral sources, four types of BRIRs are constructed and investigated. Two of them are measured on subjects with their pinnae exposed or not, and the other two are obtained by concatenating the direct and reverberant parts of the two types of measured BRIRs crosswise. Normal hearing listeners are presented with noise trains convolved with these four types of BRIRs. It is found that pinna filtering in the direct parts is helpful for externalization while pinna filtering in the reverberant parts produces less externalized sound images. Further analysis shows that pinna filtering can change the direct-to-reverberant ratio (DRR), the frequency-to-frequency variability (FFV), and the interaural coherence (IC). The DRR and the FFV are only partially related to the externalization ratings, but the short-term IC corresponds well to the ratings. This study provides guidance for improving externalization perception in binaural synthesis.

Characterizing acoustic environments using spherical loudspeaker and microphone arrays

Room acoustic parameters, including the reverberation time (T60) and the direct-to-reverberant ratio (DRR), quantitatively describe the behaviour of an acoustic environment. These parameters are typically derived from an acoustic impulse response (AIR) obtained using an omnidirectional sound source and an omnidirectional receiver. However, the acoustic interaction between a source with a complex radiation pattern, for example, a human talker, and a receiver with a complex directivity pattern, for example, a human listener, cannot be accurately described by an omnidirectional AIR or the acoustic parameters derived from it. Here, we propose characterizing acoustic environments while taking the source and receiver directionality into account. A spherical loudspeaker array allows modelling a source with an arbitrary radiation pattern. Analogously, a spherical microphone can be used to describe a receiver with an arbitrary directivity pattern. By combining spherical loudspeaker and microphone arrays, a multiple-input multiple-output (MIMO) AIR of an acoustic environment can be measured to simulate sources and receivers with arbitrary directivities and estimate acoustic parameters accordingly.

Deep recurrent neural networks based binaural speech segregation for the selection of closest target of interest

An auditory attention model that consists of binaural source segregation and also full localization of a target speech signal in a multi-talker environment is presented. The joint acoustic features, such as monaural, binaural and direct to reverberant ratio (DRR) that are successfully incorporated into deep recurrent neural network (DRNN) based joint discriminative model for the speech source segregation process. The monaural and binaural features are extracted from binaural speech mixtures of two speakers by using mean Hilbert envelope coefficients (MHEC) and interaural time, and level differences, respectively. The performance of deep recurrent network based speech segregation is validated in terms of signal to interference, signal to distortion and signal to artifacts and compared with existing architectures, including deep neural network (DNN). The proposed system is observed and found to be more suitable than monaural speech segregation especially when the desired target and interfering sources are located at different positions. The study also proposes full localization of segregated speech source that created the possibility to select the desired speaker of interest from an input acoustic speech mixture in a reverberant environment. The developed system has the capability to handle binaural segregation problem in multi-source and reverberation conditions. The auditory attention model provides accurate information about speech sources even when the desired targets are located at 2 m and above with higher reverberation time.

The effect of room acoustics on speech intelligibility and spatial release from masking

In daily life, verbal communication often takes place in indoor situations with interfering sounds, where speech intelligibility (SI) is affected by (i) masking and (ii) reverberation. Both introduce spectral and temporal changes to the signal. A critical spatial configuration to assess (binaural) SI is a frontal target speaker and two interfering sources symmetrically placed to either side (± 60°). Here a spatial release from masking (SRM) is observed in comparison to co-located frontal target and interferers, showing that the auditory system can make use of temporally fluctuating interaural differences. Room reverberation affects the temporal representation of the target and maskers and, moreover, the interaural differences depending on the spatial configuration and room acoustical properties. Here the effect of room acoustical properties (room size, T60, frequency dependency of T60), temporal structure of the interferers, and direct to reverberation ratio (DRR) on speech reception thresholds (SRT) and SRM were systematically assessed in a simulated room using headphone-based virtual acoustics. For constant T60 and DRR a different room size resulted in, e.g., significantly different SRTs but similar SRMs, implying the temporal structure of reverberation is less relevant for exploiting binaural cues. Data are discussed and compared to predictions of a binaural SI model.

The effects of diffuse noise and artificial reverberation on listener weighting of interaural cues in sound localization

The reliability of interaural time and level difference (ITD, ILD) sound location cues can be degraded by noise or reverberation. In this study, we determined how weighting of ITD and ILD varied with signal-to-noise ratio (SNR) in the presence of interaurally uncorrelated background noise and with direct-to-reverberant ratio (DRR) in the presence of artificial reverberation (generated by convolving the target signal with an interaurally uncorrelated pair of impulse responses created by multiplying Gaussian noise with a decaying exponential, RT60 = 500 ms). Wideband (0.5-16 kHz) 100-ms noise-burst targets were presented over headphones using individual head-related transfer functions. ITD and ILD were manipulated by attenuating or delaying the sound at one ear (by up to 300 μs or 10 dB), and cue weighting was computed by comparing localization response bias to imposed cue bias. Wideband (0.5-16 kHz) and low-pass (0.5-2 kHz) noise and reverberation and SNRs and DRRs from -5 to + 20 dB were used. ITD dominat...

Direct-to-Reverberant Energy Ratio Estimation Using a First-Order Microphone

The direct-to-reverberant ratio (DRR) is an important characterization of a reverberant environment. This paper presents a novel blind DRR estimation method based on the coherence function between the sound pressure and particle velocity at a point. First, a general expression of coherence function and DRR is derived in the spherical harmonic domain, without imposing assumptions on the reverberation. In this paper, DRR is expressed in terms of the coherence function as well as two parameters that are related to statistical characteristics of the reverberant environment. Then, a method to estimate the values of these two parameters using a microphone system capable of capturing first-order spherical harmonics is proposed, under three assumptions which are more realistic than the diffuse field model. Furthermore, a theoretical analysis on the use of plane wave model for direct path signal and its effect on DRR estimation is presented, and a rule of thumb is provided for determining whether the point source model should be used for the direct path signal. Finally, the ACE challenge dataset is used to validate the proposed DRR estimation method. The results show that the average full band estimation error is within 2 dB, with no clear trend of bias.

Estimating the Direct-to-Reverberant Energy Ratio Using a Spherical Harmonics-Based Spatial Correlation Model

The direct-to-reverberant ratio (DRR), which describes the energy ratio between the direct and reverberant component of a soundfield, is an important parameter in many audio applications. In this paper, we present a multichannel algorithm, which utilizes the blind recordings of a spherical microphone array to estimate the DRR of interest. The algorithm is developed based on a spatial correlation model formulated in the spherical harmonics domain. This model expresses the cross correlation matrix of the recorded soundfield coefficients in terms of two spatial correlation matrices, one for direct sound and the other for reverberation. While the direct path arrives from the source, the reverberant path is considered to be a nondiffuse soundfield with varying directional gains. The direct and reverberant sound energies are estimated from the aforementioned spatial correlation model, which then leads to the DRR estimation. The practical feasibility of the proposed algorithm was evaluated using the speech corpus of the acoustic characterization of environments challenge. The experimental results revealed that the proposed method was able to effectively estimate the DRR of a large collection of reverberant speech recordings including various environmental noise types, room types and speakers.

Estimation of Room Acoustic Parameters: The ACE Challenge

Reverberation time ( $T_{{60}}$ ) and Direct-to-reverberant ratio (DRR) are important parameters which together can characterize sound captured by microphones in nonanechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of $T_{{60}}$ and DRR can be estimated directly from the acoustic impulse response (AIR) of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The acoustic characterization of environments (ACE) challenge aimed to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that $T_{{60}}$ estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful.

Direct to reverberation ratio based two channel dereverberation for automatic speech recognition

This paper proposes a spectral subtraction based two channel dereverberation algorithm for automatic speech recognition (ASR). By observing the fact that the accuracy of ASR system with reverberant speech highly relates to the amount of reverberant component, this paper focuses on designing a novel reverberant component estimator. Especially, the proposed algorithm utilizes the relationship between direct to reverberation ratio (DRR) and the power of reverberant components, then the estimated value is further adjusted to maximize the word accuracy of recognizer. Experimental results verify that the proposed estimator improves ASR performance in various reverberant environments.

Single-Microphone LP Residual Skewness-Based Inverse Filtering of the Room Impulse Response

This paper presents a method based on higher order statistics (HOS), namely the normalized third-order moment (skewness), for blind estimation of the inverse filter of the room impulse response (RIR). Skewness is used as a measure of asymmetry, and a comprehensive comparison with the commonly used metric (kurtosis) is presented. It is shown that a sufficiently long linear predictive (LP) residual of the speech signal has an asymmetric pdf with sufficient skewness to be used as a score function for the HOS-based approach. The proposed algorithm is optimized for the inverse filter estimation problem. This optimization includes an efficient initialization for high reverberation intensities, enabling the method to be employed in highly reverberant rooms. The direct-to-reverberation ratio (DRR) as well as the equalized impulse response clearly show that our method can estimate the inverse filter even in highly reverberant environments. In addition, performance results using recorded background noise and in time-varying environments illustrate that our approach is applicable in real world situations. The proposed method is shown to be superior to the method by Wu and Wang, particularly in terms of reducing the coloration effect. Experiments under different acoustic conditions confirm the effectiveness of the proposed method for time delay estimation (TDE). Finally, the proposed algorithm is used as the first-stage of monaural segregation, and it is shown to improve the performance under different conditions.

Correlation-Based and Model-Based Blind Single-Channel Late-Reverberation Suppression in Noisy Time-Varying Acoustical Environments

This paper considers suppression of late reverberation and additive noise in single-channel speech recordings. The reverberation introduces long-term correlation in the observed signal. In the first part of this work, we show how this correlation can be used to estimate the late reverberant spectral variance (LRSV) without having to assume a specific model for the room impulse responses (RIRs) while no explicit estimates of RIR model parameters are needed. That makes this <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">correlation-based</i> approach more robust against RIR modeling errors. However, the correlation-based method can follow only slow time variations in the RIRs. Existing <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model-based</i> methods use statistical models for the RIRs, that depend on one or more parameters that have to be estimated blindly. The common statistical models lead to simple expressions for the LRSV that depend on past values of the spectral variance of the reverberant, noise-free, signal. All existing model-based LRSV estimators in the literature are derived assuming the RIRs to be <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-invariant realizations</i> of a stochastic process. In the second part of this paper, we go one step further and analyze <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-varying</i> RIRs. We show that in this case the reverberance tends to become decorrelated. We discuss the relations between different RIR models and their corresponding LRSV estimators. We show theoretically that similar simple estimators exist as in the time-invariant case, provided that the reverberation time <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</sub> and direct-to-reverberation ratio (DRR) of the RIRs remain nearly constant during an interval of the order of a few frames. We show that the reverberation time can be taken frequency-bin independent in DFT-based enhancement algorithms. Experiments with time-varying RIRs validate the analysis. Experiments with additive nonstationary noise and time-invariant RIRs show the influence of blind estimation of the reverberation time and the DRR.