The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"

Related tags

Deep LearningSF-Net
Overview

SF-Net for fullband SE

This is the repo of the manuscript "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement", which is submitted to Interspecch 2022. Some audio samples are provided here and the code for GCRN-full, DS-Net-full, CTS-Net-full and the network configuration of SF-Net are released.

Abstract:Due to the high computational complexity to model more frequency bands, it is still intractable to conduct real-time full-band speech enhancement based on deep neural networks. Recent studies typically utilize the compressed perceptually motivated features with relatively low frequency resolution to filter the full-band spectrum by one-stage networks, leading to limited speech quality improvements. In this paper, we propose a coordinated sub-band fusion network for full-band speech enhancement, which aims to recover the low- (0-8 kHz), middle- (8-16 kHz), and high-band (16-24 kHz) in a step-wise manner. Specifically, a dual-stream network is first pretrained to recover the low-band complex spectrum, and another two sub-networks are designed as the middle- and high-band noise suppressors in the magnitude-only domain. To fully capitalize on the information intercommunication, we employ a sub-band interaction module to provide external knowledge guidance across different frequency bands. Extensive experiments show that the proposed method yields consistent performance advantages over state-of-the-art full-band baselines.

Demo page of audio samples

(https://yuguochencuc.github.io/sfnet_demo/)

System flowchart of SF-Net

image

Results:

Abaltion study

lQLPDhtH56hfHqXNAjHNA7OwrtrfF1S_QEECRntyY8DWAA_947_561

Comparison with SOTA

lQLPDhtH6ITooSDNAz7NA7GwySl8YbYqe-8CRnzbbEA6AA_945_830

Visualization of spectrograms

VB dataset

lQLPDhtH6NmMG6rNAwTNA6uw8qUBkZFtUxgCRn1lwQBsAA_939_772

DNS blind set

lQLPDhtH8iT2-N_NAubNA42wBREh0WKHv5wCRoygD0BOAA_909_742

Owner
Guochen Yu
Phd student at Communication University of China and Institute of Acoustics, Chinese Academy of Sciences
Guochen Yu
PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020).

Scaffold-Federated-Learning PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020). Environment numpy=

KI 30 Dec 29, 2022
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'

Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs

5 Jul 01, 2022
Yas CRNN model training - Yet Another Genshin Impact Scanner

Yas-Train Yet Another Genshin Impact Scanner 又一个原神圣遗物导出器 介绍 该仓库为 Yas 的模型训练程序 相关资料 MobileNetV3 CRNN 使用 假设你会设置基本的pytorch环境。 生成数据集 python main.py gen 训练

wormtql 18 Jan 08, 2023
Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have underg

Nafis Ahmed 1 Dec 28, 2021
Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking Part-Aware Measurement for Robust Multi-View Multi-Human 3D P

19 Oct 27, 2022
Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

Nils Thuerey 1.3k Jan 08, 2023
Data labels and scripts for fastMRI.org

fastMRI+: Clinical pathology annotations for the fastMRI dataset The fastMRI dataset is a publicly available MRI raw (k-space) dataset. It has been us

Microsoft 51 Dec 22, 2022
[CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation

RCIL [CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation Chang-Bin Zhang1, Jia-Wen Xiao1, Xialei Liu1, Ying-Cong Chen2

Chang-Bin Zhang 71 Dec 28, 2022
ilpyt: imitation learning library with modular, baseline implementations in Pytorch

ilpyt The imitation learning toolbox (ilpyt) contains modular implementations of common deep imitation learning algorithms in PyTorch, with unified in

The MITRE Corporation 11 Nov 17, 2022
Data cleaning, missing value handle, EDA use in this project

Lending Club Case Study Project Brief Solving this assignment will give you an idea about how real business problems are solved using EDA. In this cas

Dhruvil Sheth 1 Jan 05, 2022
official implemntation for "Contrastive Learning with Stronger Augmentations"

CLSA CLSA is a self-supervised learning methods which focused on the pattern learning from strong augmentations. Copyright (C) 2020 Xiao Wang, Guo-Jun

Lab for MAchine Perception and LEarning (MAPLE) 47 Nov 29, 2022
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

Ibai Gorordo 18 Nov 06, 2022
This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

TransUNet This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Usage

1.4k Jan 04, 2023
This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

Pruning Self-attentions into Convolutional Layers in Single Path This is the official repository for our paper: Pruning Self-attentions into Convoluti

Zhuang AI Group 77 Dec 26, 2022
Sky Computing: Accelerating Geo-distributed Computing in Federated Learning

Sky Computing Introduction Sky Computing is a load-balanced framework for federated learning model parallelism. It adaptively allocate model layers to

HPC-AI Tech 72 Dec 27, 2022
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Mask R-CNN for Object Detection and Segmentation This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bound

Matterport, Inc 22.5k Jan 04, 2023
A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

Reinforcement-Learning-Notebooks A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented

Pulkit Khandelwal 1k Dec 28, 2022
Official Implementation of CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback

CoSMo.pytorch Official Implementation of CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback, Seungmin Lee*, Dongwan Kim*, Bohyung

Seung Min Lee 54 Dec 08, 2022
Official implementation of ACTION-Net: Multipath Excitation for Action Recognition (CVPR'21).

ACTION-Net Official implementation of ACTION-Net: Multipath Excitation for Action Recognition (CVPR'21). Getting Started EgoGesture data folder struct

V-Sense 171 Dec 26, 2022
Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible

Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible, to be the most reliable with the least com

Nikolas B Virionis 2 Aug 01, 2022