175 citations · 243 across the 15 of their papers we have counts for
6 papers · 1 filter
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 Metric Learning for Music Structure Features
Ju-Chiang Wang, Jordan B. L. Smith, Wei-Tsung Lu +1
Music structure analysis (MSA) methods traditionally search for musically meaningful patterns in audio: homogeneity, repetition, novelty, and segment-length regularity. Hand-crafte…
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…