25 citations · 26 across the 6 of their papers we have counts for
6 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…
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
Rui Zhou, Xian Li, Ying Fang +1
In this work, we propose Mel-FullSubNet, a single-channel Mel-spectrogram denoising and dereverberation network for improving both speech quality and automatic speech recognition (…
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…
ATST: Audio Representation Learning with Teacher-Student Transformer
Xian Li, Xiaofei Li
Self-supervised learning (SSL) learns knowledge from a large amount of unlabeled data, and then transfers the knowledge to a specific problem with a limited number of labeled data.…