We explore the use of biologically inspired auditory kernels—learned from sparse coding on (clean) read speech—to analyze and reconstruct signals degraded with additive noise. Auditory kernels mimic spectrotemporal filters in the human auditory system, offering insight into how structured acoustic signals can be internally represented and selectively preserved. Our study applies an auditory kernel-based matching pursuit reconstruction framework to clean, degraded, and standalone noise audio, investigating kernel activation patterns across input types. The findings reveal kernel selectivity; structured signals like speech activate a common subset of kernels, while unstructured noise elicits distinct, less overlapping activations, allowing for more effective separation and implicit denoising. This selectivity results in implicit denoising, preserving intelligibility and perceptual quality even under degradation. By quantifying this behavior across noise types and SNR levels, we show that auditory kernels not only support robust signal reconstruction but also offer a biologically grounded, explainable mechanism for speech enhancement. These insights advance the use of sparse auditory models in both neuroscience and signal processing, motivating future work on adaptive or context-aware dictionaries.

Abstract—Effcient neural coding is a theoretical model in sensory neuroscience, positing that biological systems maximize information transfer to the brain while minimizing neural resources. While this concept has been extensively studied in the context of human speech perception and the human brain, its applicability to non-human vocalizations remains relatively unexplored. This study applies sparse coding to bat echolocation calls and demonstrates that the resulting kernel representations exhibit properties consistent with effcient coding principles, namely high compactness, sparsity, and functional specialization. Distinct kernel activation profles were found to encode different echolocation call shapes and identify anomalous, irregular calls, indicating
that the model captures biologically relevant features and exhibits sensitivity to deviations from stereotyped call structure.
These fndings underscore the advantages of sparse coding over traditional signal representations for modeling bat vocalizations and align with evidence that effcient coding strategies are shared across mammals, tuned to species-specifc vocal patterns and conspecifc vocalizations. This work improves the interpretability of animal auditory processing and provides a computational basis for modeling mammalian vocalizations, thereby supporting further research in decoding animal
signals and interspecies communication.

Assessing speech intelligibility for individuals with dysarthria is critical for understanding the severity of motor speech disorders and evaluating speech therapy interventions. Traditional subjective as- sessments, while effective, are resource-intensive and prone to bias, which highlights the need for reliable objective measures. This study investi- gates the applicability of MIKNN (Mutual Infor- mation with K-Nearest Neighbors) as an objective speech intelligibility measure for dysarthric speech, by comparing objective intelligibility scores with subjective ratings. Unlike its proven effective- ness with neurotypical speech, the performance of objective measures on atypical speech, such as dysarthria, remains under-explored. The study compares MIKNN with state-of-the-art measures, including P-STOI and P-ESTOI, using the UA- Speech dataset. Key challenges addressed in- clude adapting MIKNN to handle the temporal and spectral variability inherent in dysarthric speech. The results demonstrate that while MIKNN offers promising correlations with subjective scores, it is outperformed by P-STOI and P-ESTOI.

Clear communication in public address systems is essential, especially in environments where safety or information clarity is critical. Speech intelligibility is often assessed using objective intelligibility metrics (OIMs), which predict intelligibility through mathematical models. These metrics perform well in non-highly reverberant conditions but face challenges in highly reverberant environments and with non-European languages like Mandarin. This study examines the performance of three intrusive OIMs—ESTOI, HASPI, and SIIB\textsubscript{Gauss}—in two aspects: (1) how these metrics perform under different reverberation conditions for English, using STIPA as a reference, and (2) how robust these metrics are by comparing the variances of scores between Mandarin and English. The results show that the variances of predicted scores by the test metrics are equal between Mandarin and English. HASPI, ESTOI, and SIIB\textsubscript{Gauss} demonstrate similar performance across a broader range of reverberation conditions (from a T60 of 0.05s to 7s) for English, contradicting the theory that most intrusive intelligibility metrics struggle with severe reverberation conditions \cite{galbrun_speech_2016}. The findings highlight the need for further research to evaluate potential biases in OIMs and their performance across languages. Incorporating listening tests could provide a more solid examination of these metrics under diverse conditions for different languages.

Objective speech intelligibility metrics (OIMs) are widely used in various fields, including public ser- vice announcements. These metrics do not directly predict the intelligibility of a speech (defined as the ratio of understandable words in an audio sample), but produce values that tend to monotonically increase with intelligibility. Several mapping functions, typically logistic models, are applied to raw objective scores to produce accurate predictions. However, there exists no standard methodology for choosing the best mapping curve, therefore, researchers tend to reuse curves originally meant for other datasets and OIMs. This research ap- plies a method called Akaike Information Criterion (AIC), specifically developed for model selection, to existing candidate models as well as new ideas based on simple heuristics. Afterwards, the models are evaluated using AIC. The new criterion affirmed the logistic mapping functions chosen for the objective intelligibility metrics STOI and MIKNN, and highlighted alternative models for the SIIB and SIIBgauss. However, with too few listening conditions on the dataset, strong inferences could not be easily made from the data.

This study investigates the performance of two objective speech quality metrics, Perceptual Evaluation of Speech Quality (PESQ) and Virtual Speech Quality Objective Listener (ViSQOL), in predicting human-rated speech quality scores, which are essential for telecommunication systems' Quality of Experience (QoE). These metrics have been validated using a limited number of languages due to the insufficiency of labeled data with human-rated scores. This research focuses on the applicability of PESQ and ViSQOL in Turkish and Korean, two languages that were not part of the validation data for calibrating these metrics.

The experiment used English as the baseline language for comparison, and the results showed that Turkish samples had higher average ViSQOL scores, with the difference being statistically significant compared to the English samples. Furthermore, Turkish male speakers had the highest correlation between PESQ and ViSQOL scores, and ViSQOL rated speech higher than PESQ, especially under babble noise degradations. Future research should focus on extending this study by exploring biases across additional metrics and languages, while also constructing a dataset with labeled subjective scores for more languages to improve the calibration of these metrics.

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.

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.

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.

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.