2 citations · 4 across the 13 of their papers we have counts for
14 papers
Joint Fullband-Subband Modeling for High-Resolution SingFake Detection
Xuanjun Chen, Chia-Yu Hu, Sung-Feng Huang +3
Rapid advances in singing voice synthesis have increased unauthorized imitation risks, creating an urgent need for better Singing Voice Deepfake (SingFake) Detection, also known as…
How Does Instrumental Music Help SingFake Detection?
Xuanjun Chen, Chia-Yu Hu, I-Ming Lin +8
Although many models exist to detect singing voice deepfakes (SingFake), how these models operate, particularly with instrumental accompaniment, is unclear. We investigate how inst…
Localizing Audio-Visual Deepfakes via Hierarchical Boundary Modeling
Xuanjun Chen, Shih-Peng Cheng, Jiawei Du +6
Audio-visual temporal deepfake localization under the content-driven partial manipulation remains a highly challenging task. In this scenario, the deepfake regions are usually only…
Dynamic Grid Trading Strategy: From Zero Expectation to Market Outperformance
Kai-Yuan Chen, Kai-Hsin Chen, Jyh-Shing Roger Jang
We propose a profitable trading strategy for the cryptocurrency market based on grid trading. Starting with an analysis of the expected value of the traditional grid strategy, we s…
Towards Generalized Source Tracing for Codec-Based Deepfake Speech
Xuanjun Chen, I-Ming Lin, Lin Zhang +3
Recent attempts at source tracing for codec-based deepfake speech (CodecFake), generated by neural audio codec-based speech generation (CoSG) models, have exhibited suboptimal perf…
Codec-Based Deepfake Source Tracing via Neural Audio Codec Taxonomy
Xuanjun Chen, I-Ming Lin, Lin Zhang +4
Recent advances in neural audio codec-based speech generation (CoSG) models have produced remarkably realistic audio deepfakes. We refer to deepfake speech generated by CoSG system…
CodecFake+: Codec-Based Resynthesized Data as a Proxy for Detecting CodecFake Speech
Xuanjun Chen, Jiawei Du, Haibin Wu +8
With the rapid advancement of neural audio codecs, codec-based speech generation (CoSG) systems have become highly powerful. Unfortunately, CoSG also enables the creation of highly…
Music2Fail: Transfer Music to Failed Recorder Style
Chon In Leong, I-Ling Chung, Kin-Fong Chao +3
The goal of music style transfer is to convert a music performance by one instrument into another while keeping the musical contents unchanged. In this paper, we investigate anothe…
DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset
Jiawei Du, I-Ming Lin, I-Hsiang Chiu +6
Mainstream zero-shot TTS production systems like Voicebox and Seed-TTS achieve human parity speech by leveraging Flow-matching and Diffusion models, respectively. Unfortunately, hu…
Improving Real-Time Music Accompaniment Separation with MMDenseNet
Chun-Hsiang Wang, Chung-Che Wang, Jun-You Wang +2
Music source separation aims to separate polyphonic music into different types of sources. Most existing methods focus on enhancing the quality of separated results by using a larg…
Neural Codec-based Adversarial Sample Detection for Speaker Verification
Xuanjun Chen, Jiawei Du, Haibin Wu +2
Automatic Speaker Verification (ASV), increasingly used in security-critical applications, faces vulnerabilities from rising adversarial attacks, with few effective defenses availa…
EMO-SUPERB: An In-depth Look at Speech Emotion Recognition
Haibin Wu, Huang-Cheng Chou, Kai-Wei Chang +5
Speech emotion recognition (SER) is a pivotal technology for human-computer interaction systems. However, 80.77% of SER papers yield results that cannot be reproduced. We develop E…
Adapting pretrained speech model for Mandarin lyrics transcription and alignment
Jun-You Wang, Chon-In Leong, Yu-Chen Lin +2
The tasks of automatic lyrics transcription and lyrics alignment have witnessed significant performance improvements in the past few years. However, most of the previous works only…
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model
Yu-Chen Lin, Akhilesh Kumar, Norman Chang +6
We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code: (i) utilizing LLM-based data splitting and data re…