14 citations · 23 across the 9 of their papers we have counts for
9 papers
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…
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…
SpecTNT: a Time-Frequency Transformer for Music Audio
Wei-Tsung Lu, Ju-Chiang Wang, Minz Won +2
Transformers have drawn attention in the MIR field for their remarkable performance shown in natural language processing and computer vision. However, prior works in the audio proc…
Supervised Chorus Detection for Popular Music Using Convolutional Neural Network and Multi-task Learning
Ju-Chiang Wang, Jordan B. L. Smith, Jitong Chen +2
This paper presents a novel supervised approach to detecting the chorus segments in popular music. Traditional approaches to this task are mostly unsupervised, with pipelines desig…