175 citations · 200 across the 7 of their papers we have counts for
9 papers
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…
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…
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
Yingjie Zhou, Xucheng Song, Yanru Zhang +3
Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks…
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…
CatNet: music source separation system with mix-audio augmentation
Xuchen Song, Qiuqiang Kong, Xingjian Du +1
Music source separation (MSS) is the task of separating a music piece into individual sources, such as vocals and accompaniment. Recently, neural network based methods have been ap…