activity
20182020
most citedSound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering

18 citations · 25 across the 4 of their papers we have counts for

collaborators

5 papers

eess.AS2020

Transfer Learning for Improving Singing-voice Detection in Polyphonic Instrumental Music

Yuanbo Hou, Frank K. Soong, Jian Luan +1

Detecting singing-voice in polyphonic instrumental music is critical to music information retrieval. To train a robust vocal detector, a large dataset marked with vocal or non-voca…

eess.AS20204 cited

Peking Opera Synthesis via Duration Informed Attention Network

Yusong Wu, Shengchen Li, Chengzhu Yu +4

Peking Opera has been the most dominant form of Chinese performing art since around 200 years ago. A Peking Opera singer usually exhibits a very strong personal style via introduci…

cs.CL20193 cited

Synthesising Expressiveness in Peking Opera via Duration Informed Attention Network

Yusong Wu, Shengchen Li, Chengzhu Yu +4

This paper presents a method that generates expressive singing voice of Peking opera. The synthesis of expressive opera singing usually requires pitch contours to be extracted as t…

cs.SD201918 cited

Sound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering

Yuanbo Hou, Qiuqiang Kong, Shengchen Li +1

Sound event detection (SED) methods typically rely on either strongly labelled data or weakly labelled data. As an alternative, sequentially labelled data (SLD) was proposed. In SL…

cs.SD2018

Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units

Yuanbo Hou, Qiuqiang Kong, Jun Wang +1

Audio tagging aims to detect the types of sound events occurring in an audio recording. To tag the polyphonic audio recordings, we propose to use Connectionist Temporal Classificat…