How DeepXi and MHANet Revolutionize Speech Enhancement with Multi‑Head Attention

DeepXi introduces a two‑stage deep learning framework for speech enhancement, using prior SNR estimation and MMSE gain, while the MHANet extension leverages multi‑head attention to model long‑range dependencies, with detailed training strategies, model compression to GRU, deployment via TFLite, and impressive low‑latency results.

Deep LearningGRUTFLite

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How DeepXi and MHANet Revolutionize Speech Enhancement with Multi‑Head Attention

360 Quality & Efficiency

Sep 3, 2021 · Artificial Intelligence

Model‑Based Audio Denoising Using Deep Learning for Device Quality Evaluation

This article presents a deep‑learning approach that transforms recorded audio into spectrograms, trains a noise‑prediction network (e.g., ResNet, U‑Net, LSTM) to estimate environmental noise, subtracts it in the frequency domain, and reconstructs a cleaner signal for more accurate audio‑device quality assessment.

Deep LearningModel TrainingSTFT

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Model‑Based Audio Denoising Using Deep Learning for Device Quality Evaluation

NetEase Smart Enterprise Tech+

Aug 23, 2021 · Artificial Intelligence

How a Lightweight Neural Network Cuts Transient Noise in Real‑Time Audio

NetEase Cloud Communication’s Audio Lab presents a low‑complexity neural‑network denoising algorithm that effectively suppresses both stationary and transient noises while preserving speech quality, detailing its mathematical model, feature design, loss function, GRU‑based architecture, real‑time performance, and comparative evaluation against state‑of‑the‑art methods.

Neural NetworkReal-time Processingaudio denoising

0 likes · 13 min read

How a Lightweight Neural Network Cuts Transient Noise in Real‑Time Audio