Reverberation Time Estimation Research Articles

A study of the spatial non-uniformity of reverberation time at low frequencies

This paper presents a study of the spatial non-uniformity of low-frequency reverberation time estimates. Using measured and simulated data, it is demonstrated that the reverberation times estimated from band-limited impulse responses vary as a function of space at low frequencies. The origin of this spatial dependency is investigated using a one-dimensional finite element model. It is shown that the Q-factor and reverberation time at the resonant frequency of a particular mode are spatially dependent and that the spatial dependency is related to the mode shapes. The ranges of the reverberation time estimates as a function of third-octave frequency bands in simulated and measured rooms are presented. An additional finding is that modal damping constants (obtained via eigenvalue analysis) quantify the decay of uncoupled modes but not necessarily the reverberation time. These observations have implications for measurements and simulations of reverberation time at low frequencies. When measuring a spatially averaged reverberation time, the choice of source and receiver positions is important. A guideline is proposed that defines a set of preferred measurement positions based on the mode shapes, from which estimates of the spatially averaged reverberation time can be obtained.

Building Design with Soundproofing Attributes

This research primarily focuses on creating the blueprint for a three-story soundproof building, specifically the Reading Hub planned for the Surigao City Tourism Office. The envisioned structure spans approximately 130 sq. m and caters to the desired interior design of the industry partner. The soundproofing component employs polyurethane, a proven and effective material in various architectural applications. The study adopts a descriptive research approach and furnishes details regarding Structural and Aesthetical Requirements, Reverberation Time, Design Criteria, and Cost Estimates. The outcomes of this design endeavor affirm its structural adequacy, aligning with the stipulations of NSCP 2015 and ACI 318. Reverberation time calculations have yielded results well within the acceptable range for an average control room volume. The estimated costs suggest that constructing a three-story building is justifiable.In summation, the design of the three-story soundproof building utilizing foam bricks and adhesive wallpaper stands as an efficacious venture. This affirmation is rooted in its meticulous evaluation and endorsement by both the industry partner and professional Engineers

Online reverberation time and clarity estimation in dynamic acoustic conditions

Previously proposed methods for estimating acoustic parameters from reverberant, noisy speech signals exhibit insufficient performance under changing acoustic conditions. A data-centric approach is proposed to overcome the limiting assumption of fixed source–receiver transmission paths. The obtained solution significantly enlarges the scope of potential applications for such estimators. The joint estimation of reverberation time RT60 and clarity index C50 in multiple frequency bands is studied with a focus on dynamic acoustic environments. Three different convolutional recurrent neural network architectures are considered to solve the tasks of single-band, multi-band, and multi-task parameter estimation. A comprehensive performance evaluation is provided that highlights the benefits of the proposed approach.

A Composite T60 Regression and Classification Approach for Speech Dereverberation

Dereverberation is often performed directly on the reverberant audio signal, without knowledge of the acoustic environment. Reverberation time, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{60}$</tex-math></inline-formula> , however, is an essential acoustic factor that reflects how reverberation may impact a signal. In this work, we propose to perform dereverberation while leveraging key acoustic information from the environment. More specifically, we develop a joint learning approach that uses a composite <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{60}$</tex-math></inline-formula> module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation. The reverberation time module provides key features to the dereverberation module during fine tuning. We evaluate our approach in simulated and real environments, and compare against several approaches. The results show that this composite framework improves performance in environments.

The Optimal Determination of the Truncation Time of Non-Exponential Sound Decays

The noise effects in the room impulse response (RIR) make the decay range of the integrated impulse response insufficient for reliable determination of reverberation time (RT). One of the preferred techniques to minimize noise effects is based on noise subtraction, RIR truncation, and correction for the truncation. The success of RT estimation through the method depends critically on the accurate estimation of the truncation time (TT). However, noise fluctuation and RIR irregularities can lead to discrepancies in the determined TT from the optimal value. The general goal of this paper is to improve RT estimates. An iterative procedure based on a non-exponential decay model consisting of a double-slope decay term and a noise term is presented to estimate the TT accurately. The model parameters are generated until the iterative procedure converges to a minimum difference between the energy decay curve (EDC) generated by the model and the Schroeder decay function. The decay rates of the EDCs with added pink noise levels are compared to those of the EDCs with low background noise. In addition, the detected TTs and the corresponding RTs are compared with the existing method and the noise compensation method (subtraction–truncation–correction method).

Noise-robust blind reverberation time estimation using noise-aware time–frequency masking

The reverberation time is one of the most important parameters used to characterize the acoustic property of an enclosure. In real-world scenarios, it is much more convenient to estimate the reverberation time blindly from recorded speech compared to the traditional acoustic measurement techniques using professional measurement instruments. However, the recorded speech is often corrupted by noise, which has a detrimental effect on the estimation accuracy of the reverberation time. To address this issue, this paper proposes a two-stage blind reverberation time estimation method based on noise-aware time–frequency masking. This proposed method has a good ability to distinguish the reverberation tails from the noise, thus improving the estimation accuracy of reverberation time in noisy scenarios. The simulated and real-world acoustic experimental results show that the proposed method significantly outperforms other methods in challenging scenarios.

Attentive Pooling-Based Weighted Sum of Spectral Decay Rates for Blind Estimation of Reverberation Time

Recently, deep learning-based methods for blind reverberation time estimation have been proposed, and outperform those based on conventional signal processing. The signal processing approaches extract the reverberant environmental features of sound by statistical analysis, while deep learning approaches train a network to capture the relationship between acoustic features and the reverberation time. In this letter, we propose a method for blind reverberation time estimation that explicitly reflects physical properties of reverberation by combining the deep learning approach, attentive pooling, and statistical characteristics of reverberant speech obtained using a signal processing method, i.e., spectral decay rates (SDRs). The results obtained with the proposed blind reverberation time estimation method are superior to the previously published state-of-the-art results for the EVAL dataset of the ACE Challenge. This work can be considered a good example of the collaboration between signal processing expertise and deep learning approach.

Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning Algorithm

The measurement of reverberation time is an essential procedure for the characterization of the acoustic performance of rooms. The values returned by these measurements allow us to predict how the sound will be transformed by the walls and furnishings of the rooms. The measurement of the reverberation time is not an easy procedure to carry out and requires the use of a space in an exclusive way. In fact, it is necessary to use instruments that reproduce a sound source and instruments for recording the response of the space. In this work, an automatic procedure for estimating the reverberation time based on the use of artificial neural networks was developed. Previously selected sounds were played, and joint sound recordings were made. The recorded sounds were processed with the extraction of characteristics, then they were labeled by associating to each sound the value of the reverberation time in octave bands of that specific room. The obtained dataset was used as input for the training of an algorithm based on artificial neural networks. The results returned by the predictive model suggest using this methodology to estimate the reverberation time of any closed space, using simple audio recordings without having to perform standard measurements or calculate the integration explicitly.

Estimation of Reverberation Time by Performing Acoustic Echo Cancellation Considering Near-end and Far-end Speech Signals

Estimation of Reverberation Time by Performing Acoustic Echo Cancellation Considering Near-end and Far-end Speech Signals

Effect of phoneme variations on blind reverberation time estimation

This study focuses on an unexplored aspect of the performance of algorithms for blind reverberation time (T) estimation – on the effect that speech signal’s phonetic content has on the value of the estimate ofTthat is obtained from the reverberant version of that signal. To this end, the performance of three algorithms is assessed on a set of logatome recordings artificially reverberated with room impulse responses from four rooms, with theirT20value in the [0.18, 0.55] s interval. Analyses of variance showed that the null hypotheses of equal means of estimation errors can be rejected at the significance level of 0.05 for the interaction terms between the factors “vowel”, “consonant”, and “room”, while the results of Tukey’s multiple comparison procedure revealed that there are both some similarities in the behaviour of the algorithms and some differences, where the latter are stemming from the differences in the details of algorithms’ implementation such as the number of frequency bands and whetherTis estimated continuously or only on the selected, the so-called speech decay, segments of the signal.

A Novel Scheme for Single-Channel Speech Dereverberation

This paper presents a novel scheme for speech dereverberation. The core of our method is a two-stage single-channel speech enhancement scheme. Degraded speech obtains a sparser representation of the linear prediction residual in the first stage of our proposed scheme by applying orthogonal matching pursuit on overcomplete bases, trained by the K-SVD algorithm. Our method includes an estimation of reverberation and mixing time from a recorded hand clap or a simulated room impulse response, which are used to create a time-domain envelope. Late reverberation is suppressed at the second stage by estimating its energy from the previous envelope and removed with spectral subtraction. Further speech enhancement is applied on minimizing the background noise, based on optimal smoothing and minimum statistics. Experimental results indicate favorable quality, compared to two state-of-the-art methods, especially in real reverberant environments with increased reverberation and background noise.

Feed forward neural network application for classroom reverberation time estimation

&lt;span&gt;Acoustic problem is a main issues of the existing classroom due to lack of absorption of surface material. Thus, a feed forward neural network system (FFNN) for classroom Reverberation Time (RT) estimation computation was built. This system was developed to assist the acoustic engineer and consultant to treat and reduce this matter. Data was collected and computed using ODEON12.10 ray tracing method, resulting in a total of 600 rectangular shaped classroom models that were modeled with various length, width, height, as well as different surface material types. The system is able to estimate RT for 500Hz, 1000Hz, and 2000Hz. Using the collected data, FFNN for each frequency were trained and simulated separately (as absorption coefficients are frequency dependent) in order to find the optimum solution. The final system was validated and compared with the actual measurement value from 15 different classrooms in Universiti Tun Hussein Onn Malaysia (UTHM). The developed system show positive results with average validation accuracy of 94.35%, 95.91%, and 96.42% for 500Hz, 1000Hz, and 2000Hz respectively. &lt;/span&gt;

Joint Estimation of Reverberation Time and Early-To-Late Reverberation Ratio From Single-Channel Speech Signals

The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters commonly used to characterize acoustic room environments. In contrast to conventional blind estimation methods that process the two parameters separately, we propose a model for joint estimation to predict the RT and the ELR simultaneously from single-channel speech signals from either full-band or sub-band frequency data, which is referred to as joint room parameter estimator (jROPE). An artificial neural network is employed to learn the mapping from acoustic observations to the RT and the ELR classes. Auditory-inspired acoustic features obtained by temporal modulation filtering of the speech time-frequency representations are used as input for the neural network. Based on an in-depth analysis of the dependency between the RT and the ELR, a two-dimensional (RT, ELR) distribution with constrained boundaries is derived, which is then exploited to evaluate four different configurations for jROPE. Experimental results show that-in comparison to the single-task ROPE system which individually estimates the RT or the ELR-jROPE provides improved results for both tasks in various reverberant and (diffuse) noisy environments. Among the four proposed joint types, the one incorporating multi-task learning with shared input and hidden layers yields the best estimation accuracies on average. When encountering extreme reverberant conditions with RTs and ELRs lying beyond the derived (RT, ELR) distribution, the type considering RT and ELR as a joint parameter performs robustly, in particular. From state-of-the-art algorithms that were tested in the acoustic characterization of environments challenge, jROPE achieves comparable results among the best for all individual tasks (RT and ELR estimation from full-band and sub-band signals).

Exploring Auditory-Inspired Acoustic Features for Room Acoustic Parameter Estimation From Monaural Speech

Room acoustic parameters that characterize acoustic environments can help to improve signal enhancement algorithms such as for dereverberation, or automatic speech recognition by adapting models to the current parameter set. The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters. In this paper, we propose a blind ROom Parameter Estimator (ROPE) based on an artificial neural network that learns the mapping to discrete ranges of the RT and the ELR from single-microphone speech signals. Auditory-inspired acoustic features are used as neural network input, which are generated by a temporal modulation filter bank applied to the speech time-frequency representation. ROPE performance is analyzed in various reverberant environments in both clean and noisy conditions for both fullband and subband RT and ELR estimations. The importance of specific temporal modulation frequencies is analyzed by evaluating the contribution of individual filters to the ROPE performance. Experimental results show that ROPE is robust against different variations caused by room impulse responses (measured versus simulated), mismatched noise levels, and speech variability reflected through different corpora. Compared to state-of-the-art algorithms that were tested in the acoustic characterisation of environments (ACE) challenge, the ROPE model is the only one that is among the best for all individual tasks (RT and ELR estimation from fullband and subband signals). Improved fullband estimations are even obtained by ROPE when integrating speech-related frequency subbands. Furthermore, the model requires the least computational resources with a real time factor that is at least two times faster than competing algorithms. Results are achieved with an average observation window of 3 s, which is important for real-time applications.

The influence of the distribution of sound absorbing materials on the estimation of reverberation time in rooms

Reverberation time in rooms depends on many factors, e.g. cubature, surface of envelopes, sound absorption coefficient of materials used for the construction of the envelopes, geometry of rooms or the distribution of sound absorbing materials. The arrangement of sound absorbing materials in rooms has an impact on the dispersion of acoustic field, yet theoretical calculation models do not take into account this impact. According to these models, regardless of the arrangement of sound absorbing materials, the reverberation time in a room will remain unchanged. The present paper investigates the above problem by means of computer simulations. For the needs of the simulation, three rooms with different dimensions were adopted, i.e. type 'p' - a cuboidal room with a square base, type 'd' - a cuboidal room (with one side of the 'p' room lengthened), type 'w' - a cuboidal room (with the height of the room lengthened 'p'). During the simulation, the way of acoustic field dispersion was being changed and its influence on the reverberation time in the rooms was being determined. The authors investigated two situations. The first one involved a non-dampened room, in which the sound absorbing material was being arranged differently. The second one involved a welldampened room, and the dispersion of sound field was analyzed depending on the location of the reflecting material.

Estimation of Reverberation Time in Classrooms Using the Residual Minimization Method

Abstract The objective of the residual minimization method is to determine a coefficient correcting the Sabine’s model. The Sabine’s equation is the most commonly applied formula in the designing process of room acoustics with the use of analytical methods. The correction of this model is indispensable for its application in rooms having non-diffusive acoustic field. The authors of the present paper will be using the residual minimization method to work out a suitable correction to be applied for classrooms. For this purpose, five different poorly dampened classrooms were selected, in which the measurements of reverberation time were carried out, and for which reverberation time was calculated with the use of theoretical methods. Three of the selected classrooms had the cubic volume of 258.5 m3 and the remaining two had the cubic volume of 190.8 m3. It was sufficient to estimate the correction for the Sabine’s equation. To verify the results, three other classrooms were selected, in which also the measurements of reverberation time were carried out. The results were verified by means of real measurements of reverberation time and by means of computer simulations in the program ODEON.

Correction of room impulse response truncation based on a nonlinear decay model

Serious effects of noise present in room impulse responses (RIRs) on evaluation of room acoustical parameters can be compensated by applying different methods. One of the preferred methods is based on the noise subtraction, RIR truncation and correction for truncation. This paper presents a procedure for calculation of the correction term that should be added to the backward integrated truncated RIR. The procedure can be applied in an automated way, and it is based on a nonlinear decay model consisting of an exponential decay plus stationary noise. Unknown parameters of the model are calculated by an optimization procedure where the model is fitted to the measurements. The proposed procedure for correction term calculation is tested on both synthesized and measured RIRs. The effects of the procedure parameters related to the RIR estimation range used for fitting the model to the measurements are analyzed. This procedure is compared with the one often applied in practice as a reference. The comparison shows that the proposed procedure outperforms the reference one. In addition, the impact of changing the correction term on the reverberation time estimation is investigated.

A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation

A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework, namely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from multiple microphones in an array and map them into the expected output LPS features of anechoic reference speech based on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at different RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed in the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments. Finally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to unseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error.

Increasing the Accuracy of Reverberation Time Measurement Using Wavelet Thresholding

Reverberation time measurement is used in building acoustic analysis and determination of acoustic characteristic of a material such as sound transmission loss and sound absorption index. This process measures the time needed for sound to decay 60 dB since the steady sound in the room is switched off. Usually, the reverberation time is estimated from sound decay curve. In the real reverberation time measurement, the smooth sound decay curve is hard to find because the original sound wave combines with reflection sound wave. In this paper, two level Daubechies wavelet transform is performed to filter noise in the sound decay. The applied wavelet thresholding successfully filtered out the maximum and minimum peak in the sound decay curve, therefore the estimation of reverberation time can be done more accurately in high and low frequency region.

Uncertainties of reverberation time estimation via adaptively identified room impulse responses.

This paper investigates the reverberation time estimation methods which employ backward integration of adaptively identified room impulse responses (RIRs). Two kinds of conditions are considered; the first is the "ideal condition" where the anechoic and reverberant signals are both known a priori so that the RIRs can be identified using system identification methods. The second is that only the reverberant speech signal is available, and blind identification of the RIRs via dereverberation is employed for reverberation time estimation. Results show that under the "ideal condition," the average relative errors in 7 octave bands are less than 2% for white noise and 15% for speech, respectively, when both the anechoic and reverberant signals are available. In contrast, under the second condition, the average relative errors of the blindly identified RIR-based reverberation time estimation are around 20%-30% except the 63 Hz octave band. The fluctuation of reverberation times estimated under the second condition is more severe than that under the ideal condition and the relative error for low frequency octave bands is larger than that for high octave bands under both conditions.