2 citations · 6 across the 5 of their papers we have counts for
5 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…
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…
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…
Modeling the Compatibility of Stem Tracks to Generate Music Mashups
Jiawen Huang, Ju-Chiang Wang, Jordan B. L. Smith +2
A music mashup combines audio elements from two or more songs to create a new work. To reduce the time and effort required to make them, researchers have developed algorithms that…
The Freesound Loop Dataset and Annotation Tool
Antonio Ramires, Frederic Font, Dmitry Bogdanov +7
Music loops are essential ingredients in electronic music production, and there is a high demand for pre-recorded loops in a variety of styles. Several commercial and community dat…