52 citations · 175 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Pac-HuBERT: Self-Supervised Music Source Separation via Primitive Auditory Clustering and Hidden-Unit BERT
Ke Chen, Gordon Wichern, François G. Germain +1
In spite of the progress in music source separation research, the small amount of publicly-available clean source data remains a constant limiting factor for performance. Thus, rec…
Closed-Loop Transcription via Convolutional Sparse Coding
Xili Dai, Ke Chen, Shengbang Tong +9
Autoencoding has achieved great empirical success as a framework for learning generative models for natural images. Autoencoders often use generic deep networks as the encoder or d…