3 citations · 3 across the 15 of their papers we have counts for
15 papers
DNSMOS-C: Improving End-to-end Speech Quality Models via Contrastive Learning
Xinyu Liang, Fredrik Cumlin, Victor Ungureanu +3
We introduce DNSMOS-C, a compact end-to-end speech quality assessment model that extends the DNSMOS Pro framework by integrating a MOS-guided triplet-based contrastive loss. Applie…
Generative Modeling for Physiological Signals
Xinqi Bao, Ernest Kamavuako, Saikat Chatterjee
Physiological signals support clinical diagnosis, health monitoring, rehabilitation, wearable sensing, and human--machine interaction. However, their applications are often constra…
Diffusion-Based Heart Sound Generation: Evaluation with Physiological Signal Metrics, Classifiers, and Expert Listening
Xinqi Bao, Jia Bi, Xin Chen +2
Publicly available phonocardiogram (PCG) datasets remain limited in size and pathological diversity, constraining both auscultation training and the generalisation of automated hea…
SA-SSL-MOS: Self-supervised Learning MOS Prediction with Spectral Augmentation for Generalized Multi-Rate Speech Assessment
Fengyuan Cao, Xinyu Liang, Fredrik Cumlin +4
Designing a speech quality assessment (SQA) system for estimating mean-opinion-score (MOS) of multi-rate speech with varying sampling frequency (16-48 kHz) is a challenging task. T…
DNS: Data-driven Nonlinear Smoother for Complex Model-free Process
Fredrik Cumlin, Anubhab Ghosh, Saikat Chatterjee
We propose data-driven nonlinear smoother (DNS) to estimate a hidden state sequence of a complex dynamical process from a noisy, linear measurement sequence. The dynamical process…
VSE: Variational state estimation of complex model-free process
Gustav Norén, Anubhab Ghosh, Fredrik Cumlin +1
We design a variational state estimation (VSE) method that provides a closed-form Gaussian posterior of an underlying complex dynamical process from (noisy) nonlinear measurements.…
pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements
Anubhab Ghosh, Yonina C. Eldar, Saikat Chatterjee
We consider the problem of designing a data-driven nonlinear state estimation (DANSE) method that uses (noisy) nonlinear measurements of a process whose underlying state transition…
Selection of Layers from Self-supervised Learning Models for Predicting Mean-Opinion-Score of Speech
Xinyu Liang, Fredrik Cumlin, Victor Ungureanu +3
Self-supervised learning (SSL) models like Wav2Vec2, HuBERT, and WavLM have been widely used in speech processing. These transformer-based models consist of multiple layers, each c…
Leveraging LLMs for Scalable Non-intrusive Speech Quality Assessment
Fredrik Cumlin, Xinyu Liang, Anubhab Ghosh +1
Non-intrusive speech quality assessment (SQA) systems suffer from limited training data and costly human annotations, hindering their generalization to real-time conferencing calls…
Multivariate Probabilistic Assessment of Speech Quality
Fredrik Cumlin, Xinyu Liang, Victor Ungureanu +3
The mean opinion score (MOS) is a standard metric for assessing speech quality, but its singular focus fails to identify specific distortions when low scores are observed. The NISQ…
Impairments are Clustered in Latents of Deep Neural Network-based Speech Quality Models
Fredrik Cumlin, Xinyu Liang, Victor Ungureanu +3
In this article, we provide an experimental observation: Deep neural network (DNN) based speech quality assessment (SQA) models have inherent latent representations where many type…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements
Anubhab Ghosh, Yonina C. Eldar, Saikat Chatterjee
We consider data-driven Bayesian state estimation from compressed measurements (BSCM) of a model-free process. The dimension of the temporal measurement vector is lower than that o…
Compressed Sensing of Generative Sparse-latent (GSL) Signals
Antoine Honoré, Anubhab Ghosh, Saikat Chatterjee
We consider reconstruction of an ambient signal in a compressed sensing (CS) setup where the ambient signal has a neural network based generative model. The generative model has a…
IMU-based Online Multi-lidar Calibration
Sandipan Das, Bengt Boberg, Maurice Fallon +1
Modern autonomous systems typically use several sensors for perception. For best performance, accurate and reliable extrinsic calibration is necessary. In this research, we propose…