1 code implementation • 22 May 2023 • Debarpan Bhattacharya, Neeraj Kumar Sharma, Debottam Dutta, Srikanth Raj Chetupalli, Pravin Mote, Sriram Ganapathy, Chandrakiran C, Sahiti Nori, Suhail K K, Sadhana Gonuguntla, Murali Alagesan
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.
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.
no code implementations • 24 Jun 2022 • Debarpan Bhattacharya, Debottam Dutta, Neeraj Kumar Sharma, Srikanth Raj Chetupalli, Pravin Mote, Sriram Ganapathy, Chandrakiran C, Sahiti Nori, Suhail K K, Sadhana Gonuguntla, Murali Alagesan
The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants.
Consider a multichannel Ambisonic recording containing a mixture of several reverberant speech signals.
1 code implementation • 9 Jun 2022 • Debarpan Bhattacharya, Debottam Dutta, Neeraj Kumar Sharma, Srikanth Raj Chetupalli, Pravin Mote, Sriram Ganapathy, Chandrakiran C, Sahiti Nori, Suhail K K, Sadhana Gonuguntla, Murali Alagesan
The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches.
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.
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.
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.
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.
The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of COVID-19 pandemic.
This paper describes the challenge submission, the post-evaluation analysis and improvements observed on the DIHARD-III dataset.
The proposed model is a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system.
no code implementations • 16 Mar 2021 • Ananya Muguli, Lancelot Pinto, Nirmala R., Neeraj Sharma, Prashant Krishnan, Prasanta Kumar Ghosh, Rohit Kumar, Shrirama Bhat, Srikanth Raj Chetupalli, Sriram Ganapathy, Shreyas Ramoji, Viral Nanda
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.