Speech Enhancement
17개 벤치마크 · 논문 1,061편 · 이 태스크의 논문 보기 →
Benchmarks
VoiceBank + DEMAND
CHiME-3
EARS-WHAM
EasyCom
VoiceBank+DEMAND
DNS Challenge
DEMAND
VB-DemandEx
WHAMR!
WSJ0 + DEMAND + RNNoise
RealMAN
LibriSpeechDuplicate
WHAM!
Most implemented
Proximal Policy Optimization Algorithms
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
SEGAN: Speech Enhancement Generative Adversarial Network
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Phase-aware Speech Enhancement with Deep Complex U-Net
Papers
Parallel Time-Band Mixing with Learned Observation-Adding for Robust ASR Front-Ends
Speech enhancement is often used as a front-end for robust ASR, yet recurrent temporal and cross-band modules introduce sequential dependencies that reduce parallel efficiency. In this paper, we present a sequence-parall…
Speech EnhancementLow-Power End-to-End Cochlear Implant Speech Denoising with Spiking Neural Networks
Cochlear implants (CI) restore hearing for individuals with severe to profound hearing loss. However, CI users often struggle to understand speech in noisy environments. Deep neural networks (DNN) have shown promise in e…
Speech EnhancementSpeech DenoisingTraining DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement
We investigate how the realism of synthetic room impulse response (RIR) datasets affects the training of DeepFilterNet3 for single-channel speech enhancement. We compare a DNS4 image-source-method (ISM) RIR dataset with …
Speech RecognitionSpeech EnhancementWeakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics
Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization. Modern beamformers are often parameterized by deep neural n…
Speech EnhancementImproving multichannel speech enhancement through accurate room-acoustic simulations
Room-acoustic simulations are widely used to augment training data for deep-learning-based speech enhancement. While most pipelines rely on simplified geometrical acoustics, wave-based approaches offer greater physical a…
Speech EnhancementSE-AGCNet: An End-to-End Framework for Joint Speech Enhancement and Loudness Control in Meeting Scenarios
Conventional audio pipelines typically treat speech enhancement (SE) and automatic gain control (AGC) as discrete modules, which often limits overall performance. For instance, applying AGC before SE may inadvertently am…
Speech Enhancement