Search Results for author:
Found 14 papers, 3 papers with code
Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
The rich metadata contained demographic information associated with age, gender and geographic location, as well as the health information relating to the symptoms, pre-existing respiratory ailments, comorbidity and SARS-CoV-2 test status.
Beamformer-Guided Target Speaker Extraction
By allowing for time-varying embeddings in the single-channel TSE block, the proposed method fully exploits the correspondence between the front-end beamformer output and the target speech in the microphone signal.
Multi-Microphone Speaker Separation by Spatial Regions
The network is trained to enforce a fixed mapping of regions to network outputs.
Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals
The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants.
AmbiSep: Ambisonic-to-Ambisonic Reverberant Speech Separation Using Transformer Networks
Consider a multichannel Ambisonic recording containing a mixture of several reverberant speech signals.
Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms
The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches.
New Insights on Target Speaker Extraction
However, such studies have been conducted on a few datasets and have not considered recent deep neural network architectures for SS that have shown impressive separation performance.
The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics
This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals.
Directional MCLP Analysis and Reconstruction for Spatial Speech Communication
Distortion-less array response constraint and the time-varying complex Gaussian source model are used in the joint estimation of source DoA and the constituent signal components, separately at each node.
Towards sound based testing of COVID-19 -- Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge
As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants.
Multi-modal Point-of-Care Diagnostics for COVID-19 Based On Acoustics and Symptoms
The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of COVID-19 pandemic.
LEAP Submission for the Third DIHARD Diarization Challenge
This paper describes the challenge submission, the post-evaluation analysis and improvements observed on the DIHARD-III dataset.
Speaker conditioned acoustic modeling for multi-speaker conversational ASR
The proposed model is a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics
The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning.
