Reverberation is a key aspect when designing the interior of buildings, and must be carefully considered in the context of the function of the room. Defined by the reverberation time (RT), it is known to have a big influence on the intelligibility and quality of audio in closed spaces.
In this work, we investigate the relationship between the RT and explore the feasibility of using multilayer perceptron (MLP) networks to create an estimator for the RT by using the values of objective measures as input features. We investigate five measures in particular: the Perceptual Evaluation of Speech Quality (PESQ), Virtual Speech Quality Objective Listener (ViSQOL) and its extension focused on audio (ViSQOLAudio), and the Short-time Objective Intelligibility Measure (STOI) and its extension ESTOI.
We create a 3-layer MLP network that estimates the RT with a mean absolute error of 0.144 on our simulated RIR test sets and 0.196 on our real RIR test set.

The accurate simulation of Room Impulse Responses (RIRs) is important in a variety of applications in acoustics such as automatic speech recognition, speech enhancement, and architectural acoustic design. While objective metrics for evaluating RIRs have been researched extensively, the subjective perceptual accuracy of the simulations is largely overlooked. This paper seeks to address this gap, designing a subjective testing methodology for evaluating the perceptual accuracy of simulated RIRs. A framework is proposed that combines the ABX testing methodology with a modified Multiple Stimuli with Hidden Reference and Anchor (MUSHRA) approach, measuring attributes such as clarity, warmth, environment, and reverbera- tion. The study involved 50 participants evaluating audio samples convolved with both real and simulated RIRs. Results seem to indicate that participants could reliably distinguish between real and simulated RIRs, with perceptual differences observed in the “clarity” and “reverberation” attributes. The findings suggest that current simulation methods for RIRs do not fully capture the perceptual aspects of acoustic environments.

A Room Impulse Response (RIR) is a mathematical model for sound propagation in a room. Estimating RIR parameters such as the reverberation time (T60) allows Automatic Speech Recognition (ASR) systems to adapt to reverberation in input signals by changing their behavior based on these estimates. Currently, machine learning techniques provide the most accurate T60 estimations. We propose a novel methodology by using intrusive Speech Intelligibility Measures (SIMs) beyond their traditional application. In this study we utilize SIIB, SIIB^Gauss, STOI and ESTOI as SIMs. For each SIM we find a best fit curve with respect to the reverberation time (T60) using a statistical approach. The statistical analysis is applied on simulated RIRs obtained by using the Image Source Method. The estimator for SIIB^Gauss achieves the lowest Mean Squared Error of 0.353 on simulated data. Although this does not outperform state-of-the-art models, we offer recommendations for possible improvements. Preliminary experiments suggest that enhancing noise robustness is crucial and that the estimators could be generalized to real-world scenarios. However, further research is necessary to confirm this.

Estimating reverberation time (RT60) accurately is crucial for enhancing the acoustic quality of various environments as it decides how you feel the sound fades away subjectively. Traditional methods, such as Sabine's equation, require extensive prior knowledge and assume ideal conditions, limiting their practicality. To address these limitations, this paper explores the application of convolutional neural networks (CNNs) enhanced with an encoder architecture based on transformer mechanisms for blind RT60 estimation. The proposed model leverages simulated and real-world datasets, incorporating environmental noise to improve robustness. Results indicate that the CNN-Encoder model achieves superior performance, with a mean squared error (MSE) as low as 0.0006 seconds for pure room impulse responses (RIRs) and 0.0011 seconds under +30dB signal-to-noise ratio (SNR) conditions. It also demonstrates potential in practical usage achieving an MSE of 0.0282 seconds under audio recordings. This approach offers a significant reduction in estimation error compared to the CNN-only architecture, demonstrating the potential for improved acoustic parameter estimation in varied environments. Future work will focus on further optimizing the model for real-world applications and reducing computational complexity while maintaining high accuracy.

In building design, it is important to consider certain materials for certain acoustical properties. Specifically, the time it takes for an audio signal to decrease in volume by 60 dB is important. This can be estimated with Sabine's and Eyring's formula's, which both make use of the average absorption coefficient of the materials in a room. This absorption coefficient indicates how much of the original audio signal is absorbed into the material. However, measuring these absorption coefficients for a material is difficult and time consuming.
In this study, a machine learning approach is used to estimate the absorption coefficients, by using the room impulse response in combination with the layout of a room. A room impulse response is the characterizing sound of a room. These two pieces of data are processed through a convolutional neural network and a multilayer perceptron, respectively, and combined to make the final prediction of absorption coefficients. A novel approach of simulating the data is used, and a real dataset is used in conjunction with the simulations to use a state-of-the-art regression loss function made for domain adaptation. The results show that the machine learning approach still has a large error compared to using Eyring's formula, and that machine learning is not yet a viable option to use instead of conventional methods.