30 citations · 149 across the 35 of their papers we have counts for
5 papers · 1 filter
Equipping Pretrained Unconditional Music Transformers with Instrument and Genre Controls
Weihan Xu, Julian McAuley, Shlomo Dubnov +1
The ''pretraining-and-finetuning'' paradigm has become a norm for training domain-specific models in natural language processing and computer vision. In this work, we aim to examin…
SelfVC: Voice Conversion With Iterative Refinement using Self Transformations
Paarth Neekhara, Shehzeen Hussain, Rafael Valle +5
We propose SelfVC, a training strategy to iteratively improve a voice conversion model with self-synthesized examples. Previous efforts on voice conversion focus on factorizing spe…
Towards Improving Harmonic Sensitivity and Prediction Stability for Singing Melody Extraction
Keren Shao, Ke Chen, Taylor Berg-Kirkpatrick +1
In deep learning research, many melody extraction models rely on redesigning neural network architectures to improve performance. In this paper, we propose an input feature modific…
MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies
Ke Chen, Yusong Wu, Haohe Liu +3
Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of…
Universal Source Separation with Weakly Labelled Data
Qiuqiang Kong, Ke Chen, Haohe Liu +4
Universal source separation (USS) is a fundamental research task for computational auditory scene analysis, which aims to separate mono recordings into individual source tracks. Th…