1 citations · 2 across the 5 of their papers we have counts for
5 papers
Mel-RoFormer for Vocal Separation and Vocal Melody Transcription
Ju-Chiang Wang, Wei-Tsung Lu, Jitong Chen
Developing a versatile deep neural network to model music audio is crucial in MIR. This task is challenging due to the intricate spectral variations inherent in music signals, whic…
Music Era Recognition Using Supervised Contrastive Learning and Artist Information
Qiqi He, Xuchen Song, Weituo Hao +3
Does popular music from the 60s sound different than that of the 90s? Prior study has shown that there would exist some variations of patterns and regularities related to instrumen…
Mel-Band RoFormer for Music Source Separation
Ju-Chiang Wang, Wei-Tsung Lu, Minz Won
Recently, multi-band spectrogram-based approaches such as Band-Split RNN (BSRNN) have demonstrated promising results for music source separation. In our recent work, we introduce t…
Scaling Up Music Information Retrieval Training with Semi-Supervised Learning
Yun-Ning Hung, Ju-Chiang Wang, Minz Won +1
In the era of data-driven Music Information Retrieval (MIR), the scarcity of labeled data has been one of the major concerns to the success of an MIR task. In this work, we leverag…
Music Source Separation with Band-Split RoPE Transformer
Wei-Tsung Lu, Ju-Chiang Wang, Qiuqiang Kong +1
Music source separation (MSS) aims to separate a music recording into multiple musically distinct stems, such as vocals, bass, drums, and more. Recently, deep learning approaches s…