activity
20132022
most citedA Hierarchical Approach to Designing Approximate Reasoning-Based Controllers for Dynamic Physical Systems

59 citations · 64 across the 4 of their papers we have counts for

collaborators

8 papers

cs.SD2022

towards automatic transcription of polyphonic electric guitar music:a new dataset and a multi-loss transformer model

Yu-Hua Chen, Wen-Yi Hsiao, Tsu-Kuang Hsieh +2

In this paper, we propose a new dataset named EGDB, that con-tains transcriptions of the electric guitar performance of 240 tab-latures rendered with different tones. Moreover, we…

cs.CV2019

k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

Yi-Lun Pan, Min-Jhih Huang, Kuo-Teng Ding +2

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identif…

cs.SD2018

Learning to match transient sound events using attentional similarity for few-shot sound recognition

Szu-Yu Chou, Kai-Hsiang Cheng, Jyh-Shing Roger Jang +1

In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be…

cs.SD2018

Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks

Cheng-Wei Wu, Jen-Yu Liu, Yi-Hsuan Yang +1

Can we make a famous rap singer like Eminem sing whatever our favorite song? Singing style transfer attempts to make this possible, by replacing the vocal of a song from the source…

cs.LG2018

Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products

Zhe-Cheng Fan, Tak-Shing T. Chan, Yi-Hsuan Yang +1

Vector-valued neural learning has emerged as a promising direction in deep learning recently. Traditionally, training data for neural networks (NNs) are formulated as a vector of s…

cs.SD20175 cited

SVSGAN: Singing Voice Separation via Generative Adversarial Network

Zhe-Cheng Fan, Yen-Lin Lai, Jyh-Shing Roger Jang

Separating two sources from an audio mixture is an important task with many applications. It is a challenging problem since only one signal channel is available for analysis. In th…