14 citations · 31 across the 15 of their papers we have counts for
5 papers · 1 filter
MuSFA: Improving Music Structural Function Analysis with Partially Labeled Data
Ju-Chiang Wang, Jordan B. L. Smith, Yun-Ning Hung
Music structure analysis (MSA) systems aim to segment a song recording into non-overlapping sections with useful labels. Previous MSA systems typically predict abstract labels in a…
Binaural Rendering of Ambisonic Signals by Neural Networks
Yin Zhu, Qiuqiang Kong, Junjie Shi +4
Binaural rendering of ambisonic signals is of broad interest to virtual reality and immersive media. Conventional methods often require manually measured Head-Related Transfer Func…
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications
Kin Wai Cheuk, Keunwoo Choi, Qiuqiang Kong +5
In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from…
Modeling Beats and Downbeats with a Time-Frequency Transformer
Yun-Ning Hung, Ju-Chiang Wang, Xuchen Song +2
Transformer is a successful deep neural network (DNN) architecture that has shown its versatility not only in natural language processing but also in music information retrieval (M…
To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions
Ju-Chiang Wang, Yun-Ning Hung, Jordan B. L. Smith
Conventional music structure analysis algorithms aim to divide a song into segments and to group them with abstract labels (e.g., 'A', 'B', and 'C'). However, explicitly identifyin…