Search Results for author: Cunhang Fan

Found 12 papers, 1 papers with code

SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection

no code implementations11 Nov 2022 Jiangyan Yi, Chenglong Wang, JianHua Tao, Zhengkun Tian, Cunhang Fan, Haoxin Ma, Ruibo Fu

A manipulated audio in the SceneFake dataset involves only tampering the acoustic scene of an utterance by using speech enhancement technologies.

Speech Enhancement

Fully Automated End-to-End Fake Audio Detection

no code implementations20 Aug 2022 Chenglong Wang, Jiangyan Yi, JianHua Tao, Haiyang Sun, Xun Chen, Zhengkun Tian, Haoxin Ma, Cunhang Fan, Ruibo Fu

The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure.

Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

no code implementations2 Aug 2022 Jun Xue, Cunhang Fan, Zhao Lv, JianHua Tao, Jiangyan Yi, Chengshi Zheng, Zhengqi Wen, Minmin Yuan, Shegang Shao

Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately.

DeepFake Detection Face Swapping

MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition

1 code implementation16 Jul 2021 Hao Chen, Ming Jin, Zhunan Li, Cunhang Fan, Jinpeng Li, Huiguang He

Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation.

Domain Adaptation EEG +2

Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning

no code implementations11 Nov 2020 Cunhang Fan, Bin Liu, JianHua Tao, Jiangyan Yi, Zhengqi Wen, Leichao Song

This paper proposes a deep time delay neural network (TDNN) for speech enhancement with full data learning.

Speech Enhancement

Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition

no code implementations9 Nov 2020 Cunhang Fan, Jiangyan Yi, JianHua Tao, Zhengkun Tian, Bin Liu, Zhengqi Wen

The joint training framework for speech enhancement and recognition methods have obtained quite good performances for robust end-to-end automatic speech recognition (ASR).

Automatic Speech Recognition Speech Enhancement +1

Simultaneous Denoising and Dereverberation Using Deep Embedding Features

no code implementations6 Apr 2020 Cunhang Fan, Jian-Hua Tao, Bin Liu, Jiangyan Yi, Zhengqi Wen

In this paper, we propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features, which is based on the deep clustering (DC).

Deep Clustering Denoising +3

Deep Attention Fusion Feature for Speech Separation with End-to-End Post-filter Method

no code implementations17 Mar 2020 Cunhang Fan, Jian-Hua Tao, Bin Liu, Jiangyan Yi, Zhengqi Wen, Xuefei Liu

Secondly, to pay more attention to the outputs of the pre-separation stage, an attention module is applied to acquire deep attention fusion features, which are extracted by computing the similarity between the mixture and the pre-separated speech.

Deep Attention Speech Separation

Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features

no code implementations23 Jul 2019 Cunhang Fan, Bin Liu, Jian-Hua Tao, Jiangyan Yi, Zhengqi Wen

Firstly, a DC network is trained to extract deep embedding features, which contain each source's information and have an advantage in discriminating each target speakers.

Deep Clustering Speech Separation

Cannot find the paper you are looking for? You can Submit a new open access paper.