pwc-archive/papers-with-abstracts huggingface dataset:
From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems
Authors: huan zhang, Jinhua Liang, Huy Phan, Wenwu Wang, Emmanouil Benetos
Date: 2025-04-30 00:00:00
Tasks: Music Generation

Evaluating generative models remains a fundamental challenge, particularly when the goal is to reflect human preferences. In this paper, we use music generation as a case study to investigate the gap between automatic evaluation metrics and human preferences. We conduct comparative experiments across five state-of-the-art music generation approaches, assessing both perceptual quality and distributional similarity to human-composed music. Specifically, we evaluate synthesis music from various perceptual dimensions and examine reference-based metrics such as Mauve Audio Divergence (MAD) and Kernel Audio Distance (KAD). Our findings reveal significant inconsistencies across the different metrics, highlighting the limitation of the current evaluation practice. To support further research, we release a benchmark dataset comprising samples from multiple models. This study provides a broader perspective on the alignment of human preference in generative modeling, advocating for more human-centered evaluation strategies across domains.
Impairments are Clustered in Latents of Deep Neural Network-based Speech Quality Models
Authors: Fredrik Cumlin, Xinyu Liang, Victor Ungureanu, Chandan K. A. Reddy, Christian Schüldt, Saikat Chatterjee
Date: 2025-04-30 00:00:00
Tasks: Classification

In this article, we provide an experimental observation: Deep neural network (DNN) based speech quality assessment (SQA) models have inherent latent representations where many types of impairments are clustered. While DNN-based SQA models are not trained for impairment classification, our experiments show good impairment classification results in an appropriate SQA latent representation. We investigate the clustering of impairments using various kinds of audio degradations that include different types of noises, waveform clipping, gain transition, pitch shift, compression, reverberation, etc. To visualize the clusters we perform classification of impairments in the SQA-latent representation domain using a standard k-nearest neighbor (kNN) classifier. We also develop a new DNN-based SQA model, named DNSMOS+, to examine whether an improvement in SQA leads to an improvement in impairment classification. The classification accuracy is 94% for LibriAugmented dataset with 16 types of impairments and 54% for ESC-50 dataset with 50 types of real noises.
Generating Localized Audible Zones Using a Single-Channel Parametric Loudspeaker
Authors: Tao Zhuang, Shaozhe Li, Feng Niu, Jia-Xin Zhong, Jing Lu
Date: 2025-04-24 00:00:00

Advanced sound zone control (SZC) techniques typically rely on massive multi-channel loudspeaker arrays to create high-contrast personal sound zones, making single-loudspeaker SZC seem impossible. In this Letter, we challenge this paradigm by introducing the multi-carrier parametric loudspeaker (MCPL), which enables SZC using only a single loudspeaker. In our approach, distinct audio signals are modulated onto separate ultrasonic carrier waves at different frequencies and combined into a single composite signal. This signal is emitted by a single-channel ultrasonic transducer, and through nonlinear demodulation in air, the audio signals interact to virtually form multi-channel outputs. This novel capability allows the application of existing SZC algorithms originally designed for multi-channel loudspeaker arrays. Simulations validate the effectiveness of our proposed single-channel MCPL, demonstrating its potential as a promising alternative to traditional multi-loudspeaker systems for achieving high-contrast SZC. Our work opens new avenues for simplifying SZC systems without compromising performance.
Perceptual Audio Coding: A 40-Year Historical Perspective
Authors: Jürgen Herre, Schuyler Quackenbush, Minje Kim, Jan Skoglund
Date: 2025-04-22 00:00:00

In the history of audio and acoustic signal processing, perceptual audio coding has certainly excelled as a bright success story by its ubiquitous deployment in virtually all digital media devices, such as computers, tablets, mobile phones, set-top-boxes, and digital radios. From a technology perspective, perceptual audio coding has undergone tremendous development from the first very basic perceptually driven coders (including the popular mp3 format) to today's full-blown integrated coding/rendering systems. This paper provides a historical overview of this research journey by pinpointing the pivotal development steps in the evolution of perceptual audio coding. Finally, it provides thoughts about future directions in this area.
FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning
Authors: Ju Yeon Kang, Ji Won Yoon, Semin Kim, Min Hyun Han, Nam Soo Kim
Date: 2025-04-22 00:00:00
Tasks: Deep Learning, Speaker Verification, Speech Synthesis, Voice Conversion

Recently, fake audio detection has gained significant attention, as advancements in speech synthesis and voice conversion have increased the vulnerability of automatic speaker verification (ASV) systems to spoofing attacks. A key challenge in this task is generalizing models to detect unseen, out-of-distribution (OOD) attacks. Although existing approaches have shown promising results, they inherently suffer from overconfidence issues due to the usage of softmax for classification, which can produce unreliable predictions when encountering unpredictable spoofing attempts. To deal with this limitation, we propose a novel framework called fake audio detection with evidential learning (FADEL). By modeling class probabilities with a Dirichlet distribution, FADEL incorporates model uncertainty into its predictions, thereby leading to more robust performance in OOD scenarios. Experimental results on the ASVspoof2019 Logical Access (LA) and ASVspoof2021 LA datasets indicate that the proposed method significantly improves the performance of baseline models. Furthermore, we demonstrate the validity of uncertainty estimation by analyzing a strong correlation between average uncertainty and equal error rate (EER) across different spoofing algorithms.
Quantitative Measures for Passive Sonar Texture Analysis
Authors: Jarin Ritu, Alexandra Van Dine, Joshua Peeples
Date: 2025-04-21 00:00:00
Tasks: Texture Classification

Passive sonar signals contain complex characteristics often arising from environmental noise, vessel machinery, and propagation effects. While convolutional neural networks (CNNs) perform well on passive sonar classification tasks, they can struggle with statistical variations that occur in the data. To investigate this limitation, synthetic underwater acoustic datasets are generated that centered on amplitude and period variations. Two metrics are proposed to quantify and validate these characteristics in the context of statistical and structural texture for passive sonar. These measures are applied to real-world passive sonar datasets to assess texture information in the signals and correlate the performances of the models. Results show that CNNs underperform on statistically textured signals, but incorporating explicit statistical texture modeling yields consistent improvements. These findings highlight the importance of quantifying texture information for passive sonar classification.
DNN based HRIRs Identification with a Continuously Rotating Speaker Array
Authors: Byeong-Yun Ko, Deokki Min, Hyeonuk Nam, Yong-Hwa Park
Date: 2025-04-21 00:00:00

Conventional static measurement of head-related impulse responses (HRIRs) is time-consuming due to the need for repositioning a speaker array for each azimuth angle. Dynamic approaches using analytical models with a continuously rotating speaker array have been proposed, but their accuracy is significantly reduced at high rotational speeds. To address this limitation, we propose a DNN-based HRIRs identification using sequence-to-sequence learning. The proposed DNN model incorporates fully connected (FC) networks to effectively capture HRIR transitions and includes reset and update gates to identify HRIRs over a whole sequence. The model updates the HRIRs vector coefficients based on the gradient of the instantaneous square error (ISE). Additionally, we introduce a learnable normalization process based on the speaker excitation signals to stabilize the gradient scale of ISE across time. A training scheme, referred to as whole-sequence updating and optimization scheme, is also introduced to prevent overfitting. We evaluated the proposed method through simulations and experiments. Simulation results using the FABIAN database show that the proposed method outperforms previous analytic models, achieving over 7 dB improvement in normalized misalignment (NM) and maintaining log spectral distortion (LSD) below 2 dB at a rotational speed of 45{\deg}/s. Experimental results with a custom-built speaker array confirm that the proposed method successfully preserved accurate sound localization cues, consistent with those from static measurement. Source code is available at https://github.com/byko0810/DNN-based-HRIRs-identification
Predicting speech intelligibility in older adults using the Gammachirp Envelope Similarity Index, GESI
Authors: Ayako Yamamoto, Fuki Miyazaki, Toshio Irino
Date: 2025-04-20 00:00:00
Tasks: Speech Enhancement

We propose an objective intelligibility measure (OIM), called the Gammachirp Envelope Similarity Index (GESI), that can predict speech intelligibility (SI) in older adults. GESI is a bottom-up model based on psychoacoustic knowledge from the peripheral to the central auditory system and requires no training data. It computes the single SI metric using the gammachirp filterbank (GCFB), the modulation filterbank, and the extended cosine similarity measure. It takes into account not only the hearing level represented in the audiogram, but also the temporal processing characteristics captured by the temporal modulation transfer function (TMTF). To evaluate performance, SI experiments were conducted with older adults of various hearing levels using speech-in-noise with ideal speech enhancement on familiarity-controlled words. The prediction performance was compared with HASPIw2, which was developed for keyword SI prediction. The results showed that GESI predicted the subjective SI scores more accurately than HASPIw2. The effect of introducing TMTF into the GESI algorithm was not significant, indicating that more research is needed to know how to introduce temporal response characteristics into the OIM.
Discovering phoneme-specific critical articulators through a data-driven approach
Authors: Jesuraj Bandekar, Sathvik Udupa, Prasanta Kumar Ghosh
Date: 2025-04-15 00:00:00
Tasks: Prediction

We propose an approach for learning critical articulators for phonemes through a machine learning approach. We formulate the learning with three models trained end to end. First, we use Acoustic to Articulatory Inversion (AAI) to predict time-varying speech articulators EMA. We also predict the phoneme-specific weights across articulators for each frame. To avoid overfitting, we also add a dropout layer before the weights prediction layer. Next, we normalize the predicted weights across articulators using min-max normalization for each frame. The normalized weights are multiplied by the ground truth $EMA$ and then we try to predict the phones at each frame. We train this whole setup end to end and use two losses. One loss is for the phone prediction which is the cross entropy loss and the other is for the AAI prediction which is the mean squared error loss. To maintain gradient flow between the phone prediction block and the $EMA$ prediction block, we use straight-through estimation. The goal here is to predict the weights of the articulator at each frame while training the model end to end.
AI-Driven Segmentation and Analysis of Microbial Cells
Authors: Shuang Zhang, Carleton Coffin, Karyn L. Rogers, Catherine Ann Royer, Ge Wang
Date: 2025-05-01 00:00:00
Tasks: Denoising, Segmentation, Zero Shot Segmentation

Studying the growth and metabolism of microbes provides critical insights into their evolutionary adaptations to harsh environments, which are essential for microbial research and biotechnology applications. In this study, we developed an AI-driven image analysis system to efficiently segment individual cells and quantitatively analyze key cellular features. This system is comprised of four main modules. First, a denoising algorithm enhances contrast and suppresses noise while preserving fine cellular details. Second, the Segment Anything Model (SAM) enables accurate, zero-shot segmentation of cells without additional training. Third, post-processing is applied to refine segmentation results by removing over-segmented masks. Finally, quantitative analysis algorithms extract essential cellular features, including average intensity, length, width, and volume. The results show that denoising and post-processing significantly improved the segmentation accuracy of SAM in this new domain. Without human annotations, the AI-driven pipeline automatically and efficiently outlines cellular boundaries, indexes them, and calculates key cellular parameters with high accuracy. This framework will enable efficient and automated quantitative analysis of high-resolution fluorescence microscopy images to advance research into microbial adaptations to grow and metabolism that allow extremophiles to thrive in their harsh habitats.
Lane-Wise Highway Anomaly Detection
Authors: Mei Qiu, William Lorenz Reindl, Yaobin Chen, Stanley Chien, Shu Hu
Date: 2025-05-05 00:00:00
Tasks: Anomaly Detection

This paper proposes a scalable and interpretable framework for lane-wise highway traffic anomaly detection, leveraging multi-modal time series data extracted from surveillance cameras. Unlike traditional sensor-dependent methods, our approach uses AI-powered vision models to extract lane-specific features, including vehicle count, occupancy, and truck percentage, without relying on costly hardware or complex road modeling. We introduce a novel dataset containing 73,139 lane-wise samples, annotated with four classes of expert-validated anomalies: three traffic-related anomalies (lane blockage and recovery, foreign object intrusion, and sustained congestion) and one sensor-related anomaly (camera angle shift). Our multi-branch detection system integrates deep learning, rule-based logic, and machine learning to improve robustness and precision. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods in precision, recall, and F1-score, providing a cost-effective and scalable solution for real-world intelligent transportation systems.
Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systems
Authors: Tal Oved, Beatrice Lena, Chloé F. Najac, Sheng Shen, Matthew S. Rosen, Andrew Webb, Efrat Shimron
Date: 2025-05-05 00:00:00
Tasks: Diagnostic

Magnetic resonance imaging (MRI) offers superb-quality images, but its accessibility is limited by high costs, posing challenges for patients requiring longitudinal care. Low-field MRI provides affordable imaging with low-cost devices but is hindered by long scans and degraded image quality, including low signal-to-noise ratio (SNR) and tissue contrast. We propose a novel healthcare paradigm: using deep learning to extract personalized features from past standard high-field MRI scans and harnessing them to enable accelerated, enhanced-quality follow-up scans with low-cost systems. To overcome the SNR and contrast differences, we introduce ViT-Fuser, a feature-fusion vision transformer that learns features from past scans, e.g. those stored in standard DICOM CDs. We show that \textit{a single prior scan is sufficient}, and this scan can come from various MRI vendors, field strengths, and pulse sequences. Experiments with four datasets, including glioblastoma data, low-field ($50mT$), and ultra-low-field ($6.5mT$) data, demonstrate that ViT-Fuser outperforms state-of-the-art methods, providing enhanced-quality images from accelerated low-field scans, with robustness to out-of-distribution data. Our freely available framework thus enables rapid, diagnostic-quality, low-cost imaging for wide healthcare applications.
OASIS: Optimized Lightweight Autoencoder System for Distributed In-Sensor computing
Authors: Chengwei Zhou, Sreetama Sarkar, Yuming Li, Arnab Sanyal, Gourav Datta
Date: 2025-05-04 00:00:00
Tasks: Dimensionality Reduction

In-sensor computing, which integrates computation directly within the sensor, has emerged as a promising paradigm for machine vision applications such as AR/VR and smart home systems. By processing data on-chip before transmission, it alleviates the bandwidth bottleneck caused by high-resolution, high-frame-rate image transmission, particularly in video applications. We envision a system architecture that integrates a CMOS image sensor (CIS) with a logic chip via advanced packaging, where the logic chip processes early-stage deep neural network (DNN) layers. However, its limited compute and memory make deploying advanced DNNs challenging. A simple solution is to split the model, executing the first part on the logic chip and the rest off-chip. However, modern DNNs require multiple layers before dimensionality reduction, limiting their ability to achieve the primary goal of in-sensor computing: minimizing data bandwidth. To address this, we propose a dual-branch autoencoder-based vision architecture that deploys a lightweight encoder on the logic chip while the task-specific network runs off-chip. The encoder is trained using a triple loss function: (1) task-specific loss to optimize accuracy, (2) entropy loss to enforce compact and compressible representations, and (3) reconstruction loss (mean-square error) to preserve essential visual information. This design enables a four-order-of-magnitude reduction in output activation dimensionality compared to input images, resulting in a $2{-}4.5\times$ decrease in energy consumption, as validated by our hardware-backed semi-analytical energy models. We evaluate our approach on CNN and ViT-based models across applications in smart home and augmented reality domains, achieving state-of-the-art accuracy with energy efficiency of up to 22.7 TOPS/W.
Make Both Ends Meet: A Synergistic Optimization Infrared Small Target Detection with Streamlined Computational Overhead
Authors: Yuxin Jing, Yuchen Zheng, Jufeng Zhao, Guangmang Cui, Tianpei Zhang
Date: 2025-04-30 00:00:00
Tasks: Computational Efficiency, Semantic Segmentation

Infrared small target detection(IRSTD) is widely recognized as a challenging task due to the inherent limitations of infrared imaging, including low signal-to-noise ratios, lack of texture details, and complex background interference. While most existing methods model IRSTD as a semantic segmentation task, but they suffer from two critical drawbacks: (1)blurred target boundaries caused by long-distance imaging dispersion; and (2) excessive computational overhead due to indiscriminate feature stackin. To address these issues, we propose the Lightweight Efficiency Infrared Small Target Detection (LE-IRSTD), a lightweight and efficient framework based on YOLOv8n, with following key innovations. Firstly, we identify that the multiple bottleneck structures within the C2f component of the YOLOv8-n backbone contribute to an increased computational burden. Therefore, we implement the Mobile Inverted Bottleneck Convolution block (MBConvblock) and Bottleneck Structure block (BSblock) in the backbone, effectively balancing the trade-off between computational efficiency and the extraction of deep semantic information. Secondly, we introduce the Attention-based Variable Convolution Stem (AVCStem) structure, substituting the final convolution with Variable Kernel Convolution (VKConv), which allows for adaptive convolutional kernels that can transform into various shapes, facilitating the receptive field for the extraction of targets. Finally, we employ Global Shuffle Convolution (GSConv) to shuffle the channel dimension features obtained from different convolutional approaches, thereby enhancing the robustness and generalization capabilities of our method. Experimental results demonstrate that our LE-IRSTD method achieves compelling results in both accuracy and lightweight performance, outperforming several state-of-the-art deep learning methods.
EMulator: Rapid Estimation of Complex-valued Electric Fields using a U-Net Architecture
Authors: Fatima Ahsan, Lorenzo Luzi, Richard G. Barainuk, Sameer A. Sheth, Wayne Goodman, Behnaam Aazhang
Date: 2025-05-04 00:00:00

A common factor across electromagnetic methodologies of brain stimulation is the optimization of essential dosimetry parameters, like amplitude, phase, and location of one or more transducers, which controls the stimulation strength and targeting precision. Since obtaining in-vivo measurements for the electric field distribution inside the biological tissue is challenging, physics-based simulators are used. However, these simulators are computationally expensive and time-consuming, making repeated calculations of electric fields for optimization purposes computationally prohibitive. To overcome this issue, we developed EMulator, a U-Net architecture-based regression model, for fast and robust complex electric field estimation. We trained EMulator using electric fields generated by 43 antennas placed around 14 segmented human brain models. Once trained, EMulator uses a segmented human brain model with an antenna location as an input and outputs the corresponding electric field. A representative result of our study is that, at 1.5 GHz, on the validation dataset consisting of 6 subjects, we can estimate the electric field with the magnitude of complex correlation coefficient of 0.978. Additionally, we could calculate the electric field with a mean time of 4.4 ms. On average, this is at least x1200 faster than the time required by state-of-the-art physics-based simulator COMSOL. The significance of this work is that it shows the possibility of real-time calculation of the electric field from the segmented human head model and antenna location, making it possible to optimize the amplitude, phase, and location of several different transducers with stochastic gradient descent since our model is almost everywhere differentiable.
Impact of Chirality on the Properties of Two-Dimensional Images Propagating Through a Chiral Dispersive Thick Lens
Authors: Salaheddeen Bugoffa, Hussin Ragb
Date: 2025-05-03 00:00:00

Dual image formation for a two-dimensional object via bimodal propagation through chiral-dispersive thick lens is derived. In this article, first-order frequency-dependent material dispersion of the dielectric permittivity and the lens material being chiral are considered. In addition, the thick lens is configured in a uniform background. A salient feature of a chiral thick lens is the inherent bimodal propagation via circular polarizations. Under chirality, two sets of ABCD frequency dependent matrices are derived for right- and left-circularly polarized modes based on standard paraxial and meridional conditions. For imaging purposes, a simple2D colored transparency is placed as an object before the thick lens. The image transmission across the lens examined via the ABCD matrix parameters and defocusing effects due to dispersion under different chirality bands.