12 citations · 27 across the 6 of their papers we have counts for
6 papers
Elucidate Gender Fairness in Singing Voice Transcription
Xiangming Gu, Wei Zeng, Ye Wang
It is widely known that males and females typically possess different sound characteristics when singing, such as timbre and pitch, but it has never been explored whether these gen…
Deep Audio-Visual Singing Voice Transcription based on Self-Supervised Learning Models
Xiangming Gu, Wei Zeng, Jianan Zhang +2
Singing voice transcription converts recorded singing audio to musical notation. Sound contamination (such as accompaniment) and lack of annotated data make singing voice transcrip…
Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription
Longshen Ou, Xiangming Gu, Ye Wang
Automatic speech recognition (ASR) has progressed significantly in recent years due to the emergence of large-scale datasets and the self-supervised learning (SSL) paradigm. Howeve…
MM-ALT: A Multimodal Automatic Lyric Transcription System
Xiangming Gu, Longshen Ou, Danielle Ong +1
Automatic lyric transcription (ALT) is a nascent field of study attracting increasing interest from both the speech and music information retrieval communities, given its significa…
Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization
Wei Wei, Huang Hengguan, Gu Xiangming +2
Content mismatch usually occurs when data from one modality is translated to another, e.g. language learners producing mispronunciations (errors in speech) when reading a sentence…
Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System
Xiangming Gu, Xiang Cheng
Deep neural networks (DNNs) demonstrate great success in classification tasks. However, they act as black boxes and we don't know how they make decisions in a particular classifica…