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A set of routines that simulate array responses for sensors with arbitrary geometry and directional characteristics.
A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.
Source code of the DCASE 2020 SELD submission "Audio Event Detection and Localization with Multitask Regression Network"
Code for the paper Hybrid Spectrogram and Waveform Source Separation
Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.
A fast implementation of bss_eval metrics for blind source separation
Latte: Cross-framework Python Package for Evaluation of Latent-based Generative Models
Manipulate audio with a simple and easy high level interface
The PyTorch-based audio source separation toolkit for researchers
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Self-Attention Generative Adversarial Network for Speech Enhancement using Tensorflow 2
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
Template for data generator with PyTorch
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
Implementation for PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (CVPR 2020)
Python program for reading and writing multi-channel audio input stream
PyTorch Tutorial for Deep Learning Researchers
A two-stage polyphonic sound event detection and localization method for both SED and DOA.
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).