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Found 8 papers, 3 papers with code
MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
In this study, we propose a multi-branched speech intelligibility prediction model (MBI-Net), for predicting the subjective intelligibility scores of HA users.
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model
Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention.
Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features
In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
Learning Unsupervised Metaformer for Anomaly Detection
Anomaly detection (AD) aims to address the task of classification or localization of image anomalies.
Speech Enhancement with Zero-Shot Model Selection
Experimental results confirmed that the proposed ZMOS approach can achieve better performance in both seen and unseen noise types compared to the baseline systems and other model selection systems, which indicates the effectiveness of the proposed approach in providing robust SE performance.
STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model
To overcome this limitation, we propose a deep learning-based non-intrusive speech intelligibility assessment model, namely STOI-Net.
Dimensionality Reduction for Data in Multiple Feature Representations
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance.
