1 citations · 1 across the 7 of their papers we have counts for
7 papers
Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss
Nian Shao, Xian Li, Xiaofei Li
Sound event detection (SED) is a core module for acoustic environmental analysis, yet its performance is often limited by scarce labeled data. Recent systems leverage large pretrai…
CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
Nian Shao, Rui Zhou, Pengyu Wang +4
In this work, we propose CleanMel, a single-channel Mel-spectrogram denoising and dereverberation network for improving both speech quality and automatic speech recognition (ASR) p…
RealMAN: A Real-Recorded and Annotated Microphone Array Dataset for Dynamic Speech Enhancement and Localization
Bing Yang, Changsheng Quan, Yabo Wang +7
The training of deep learning-based multichannel speech enhancement and source localization systems relies heavily on the simulation of room impulse response and multichannel diffu…
Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors
Di Liang, Nian Shao, Xiaofei Li
This work proposes a frame-wise online/streaming end-to-end neural diarization (FS-EEND) method in a frame-in-frame-out fashion. To frame-wisely detect a flexible number of speaker…
Fine-tune the pretrained ATST model for sound event detection
Nian Shao, Xian Li, Xiaofei Li
Sound event detection (SED) often suffers from the data deficiency problem. The recent baseline system in the DCASE2023 challenge task 4 leverages the large pretrained self-supervi…
Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
Xian Li, Nian Shao, Xiaofei Li
Self-supervised learning (SSL) has emerged as a popular approach for learning audio representations. One goal of audio self-supervised pre-training is to transfer knowledge to down…
RCT: Random Consistency Training for Semi-supervised Sound Event Detection
Nian Shao, Erfan Loweimi, Xiaofei Li
Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency. The integration of semi-supervised learning (SSL) lar…