activity
20162023
most citedIs Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain

70 citations · 222 across the 40 of their papers we have counts for

collaborators

40 papers

cs.SD2023★ 3 cited

Amphion: An Open-Source Audio, Music and Speech Generation Toolkit

Xueyao Zhang, Liumeng Xue, Yicheng Gu +16

Amphion is an open-source toolkit for Audio, Music, and Speech Generation, targeting to ease the way for junior researchers and engineers into these fields. It presents a unified f…

eess.AS2023

Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition

Shuai Wang, Qibing Bai, Qi Liu +5

Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets. To boost the system performance, researchers leverage lar…

cs.SD2023

Audio-Visual Active Speaker Extraction for Sparsely Overlapped Multi-talker Speech

Junjie Li, Ruijie Tao, Zexu Pan +3

Target speaker extraction aims to extract the speech of a specific speaker from a multi-talker mixture as specified by an auxiliary reference. Most studies focus on the scenario wh…

cs.SD2023

USED: Universal Speaker Extraction and Diarization

Junyi Ao, Mehmet Sinan Yıldırım, Ruijie Tao +4

Speaker extraction and diarization are two enabling techniques for real-world speech applications. Speaker extraction aims to extract a target speaker's voice from a speech mixture…

cs.SD2023★ 1 cited

Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks

Zeyang Song, Jibin Wu, Malu Zhang +2

Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing. However, their performance in speech processing remains limited due…

cs.NE2023

TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling

Shimin Zhang, Qu Yang, Chenxiang Ma +3

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a…

cs.NE2023★ 5 cited

Long Short-term Memory with Two-Compartment Spiking Neuron

Shimin Zhang, Qu Yang, Chenxiang Ma +3

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a…

eess.AS2023

Target Active Speaker Detection with Audio-visual Cues

Yidi Jiang, Ruijie Tao, Zexu Pan +1

In active speaker detection (ASD), we would like to detect whether an on-screen person is speaking based on audio-visual cues. Previous studies have primarily focused on modeling a…

cs.SD2023★ 1 cited

Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion

Rui Liu, Jinhua Zhang, Guanglai Gao +1

Audio Deepfake Detection (ADD) aims to detect the fake audio generated by text-to-speech (TTS), voice conversion (VC) and replay, etc., which is an emerging topic. Traditionally we…

cs.SD2023★ 20 cited

ADD 2023: the Second Audio Deepfake Detection Challenge

Jiangyan Yi, Jianhua Tao, Ruibo Fu +15

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the…

cs.SD2023

Ripple sparse self-attention for monaural speech enhancement

Qiquan Zhang, Hongxu Zhu, Qi Song +3

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally…

cs.NE2023★ 2 cited

A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks

Xinyi Chen, Qu Yang, Jibin Wu +2

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneo…

eess.AS2022

ImagineNET: Target Speaker Extraction with Intermittent Visual Cue through Embedding Inpainting

Zexu Pan, Wupeng Wang, Marvin Borsdorf +1

The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target s…

eess.AS2022

Speaker recognition with two-step multi-modal deep cleansing

Ruijie Tao, Kong Aik Lee, Zhan Shi +1

Neural network-based speaker recognition has achieved significant improvement in recent years. A robust speaker representation learns meaningful knowledge from both hard and easy s…

cs.SD2022★ 3 cited

Controllable Accented Text-to-Speech Synthesis

Rui Liu, Berrak Sisman, Guanglai Gao +1

Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). Accented TTS synthesis is challenging as L2 is diffe…

cs.MM2022★ 6 cited

Predict-and-Update Network: Audio-Visual Speech Recognition Inspired by Human Speech Perception

Jiadong Wang, Xinyuan Qian, Haizhou Li

Audio and visual signals complement each other in human speech perception, so do they in speech recognition. The visual hint is less evident than the acoustic hint, but more robust…

eess.AS2022

A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain Target Speaker Extraction

Zexu Pan, Meng Ge, Haizhou Li

The speaker extraction algorithm extracts the target speech from a mixture speech containing interference speech and background noise. The extraction process sometimes over-suppres…

eess.AS2022

L-SpEx: Localized Target Speaker Extraction

Meng Ge, Chenglin Xu, Longbiao Wang +3

Speaker extraction aims to extract the target speaker's voice from a multi-talker speech mixture given an auxiliary reference utterance. Recent studies show that speaker extraction…

cs.SD2022★ 1 cited

ADD 2022: the First Audio Deep Synthesis Detection Challenge

Jiangyan Yi, Ruibo Fu, Jianhua Tao +17

Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios.…

cs.SD2022★ 1 cited

Noise-robust voice conversion with domain adversarial training

Hongqiang Du, Lei Xie, Haizhou Li

Voice conversion has made great progress in the past few years under the studio-quality test scenario in terms of speech quality and speaker similarity. However, in real applicatio…

cs.NE2020★ 1 cited

Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks

Malu Zhang, Jiadong Wang, Burin Amornpaisannon +8

Spiking Neural Networks (SNNs) use spatio-temporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-po…

eess.AS2019★ 5 cited

Acoustic Modeling for Automatic Lyrics-to-Audio Alignment

Chitralekha Gupta, Emre Yılmaz, Haizhou Li

Automatic lyrics to polyphonic audio alignment is a challenging task not only because the vocals are corrupted by background music, but also there is a lack of annotated polyphonic…

cs.CL2019★ 12 cited

VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019

Andros Tjandra, Berrak Sisman, Mingyang Zhang +3

We describe our submitted system for the ZeroSpeech Challenge 2019. The current challenge theme addresses the difficulty of constructing a speech synthesizer without any text or ph…

eess.AS2019★ 3 cited

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen +43

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which…