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Found 4 papers, 2 papers with code
The USTC-Ximalaya system for the ICASSP 2022 multi-channel multi-party meeting transcription (M2MeT) challenge
We propose two improvements to target-speaker voice activity detection (TS-VAD), the core component in our proposed speaker diarization system that was submitted to the 2022 Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenge.
USTC-NELSLIP System Description for DIHARD-III Challenge
This system description describes our submission system to the Third DIHARD Speech Diarization Challenge.
A Two-Stage Approach to Device-Robust Acoustic Scene Classification
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed.
Ranked #1 on
Acoustic Scene Classification
on TAU Urban Acoustic Scenes 2019
(using extra training data)
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
On Task 1b development data set, we achieve an accuracy of 96. 7\% with a model size smaller than 500KB.
