5 citations · 9 across the 2 of their papers we have counts for
3 papers
cs.SD2021★ 4 cited
Transfer Learning for Piano Sustain-Pedal Detection
Beici Liang, György Fazekas, Mark Sandler
Detecting piano pedalling techniques in polyphonic music remains a challenging task in music information retrieval. While other piano-related tasks, such as pitch estimation and on…
cs.SD2020★ 5 cited
Learning Audio Embeddings with User Listening Data for Content-based Music Recommendation
Ke Chen, Beici Liang, Xiaoshuan Ma +1
Personalized recommendation on new track releases has always been a challenging problem in the music industry. To combat this problem, we first explore user listening history and d…
eess.AS2020
Phase-aware music super-resolution using generative adversarial networks
Shichao Hu, Bin Zhang, Beici Liang +2
Audio super-resolution is a challenging task of recovering the missing high-resolution features from a low-resolution signal. To address this, generative adversarial networks (GAN)…