canbeNb

canbeNb's Stars

• peiss/ant-learn-pandas

  pandas学习课程代码仓库

  663383603

• aishoot/Sound_Localization_Algorithms

  Classical algorithms of sound source localization with beamforming, TDOA and high-resolution spectral estimation.

  Language:Jupyter Notebook390140101

• WenzheLiu-Speech/sound-source-localization-algorithm_DOA_estimation

  关于语音信号声源定位DOA估计所用的一些传统算法

  Language:MATLAB3846784

• xiongyihui/tdoa

  TDOA based on GCC-PHAT

  Language:Python1736865

• shvdiwnkozbw/Multi-Source-Sound-Localization

  This repo aims to perform sound localization in complex audiovisual scenes, where there multiple objects making sounds.

  Language:Python8351015

• GAMMA-UMD/doa-release

  A Direction-of-Arrival estimation code repo accompanying our research paper.

  Language:Python712220

• xuchenglin28/WSCM-MUSIC

  Weighted Spatial Covariance Matrix Estimation for MUSIC based TDOA Estimation of Speech Source

  Language:MATLAB704023

• giusenso/seld-tcn

  SELD-TCN: Sound Event Detection & Localization via Temporal Convolutional Network | Python w/ Tensorflow

  Language:Python614718

• DTaoo/Discriminative-Sounding-Objects-Localization

  Code for Discriminative Sounding Objects Localization (NeurIPS 2020)

  Language:Python584169

• SusannaWull/speechFeatures

  语音处理，声源定位中的一些基本特征

  Language:Python471014

• jewhoguy/SRP-PHAT

  Sound source localization using SRP-PHAT

  Language:Python23106

• Fhrozen/jrm_ssl

  Files for the paper: "Sound Source Localization using Deep Residual Learning"

  Language:Python22305

• ishaaniwani/GCC-PHAT-SSL

  MATLAB Simulation Framework For Basic Sound Source Localization Using the GCC PHAT Algorithm

  Language:MATLAB19102

• RickyMexx/3D-Sound-Localization

  Quaternion Neural Networks for 3D Sound Source Localization in Reverberant Environments.

  Language:Python19204

• matousc89/signalz

  Data generators in Python

  Language:Python14413

• amjadsaadeh/pyMUSIC

  Simple implementation of MUltiple SIgnal Classification

  Language:Python135

• dbklim/WebRTCVAD_Wrapper

  A simple Python wrapper to simplify working with WebRTC VAD and its rougher analogue based on RMS and ZCR (useful for processing audio recordings before using them with neural networks).

  Language:Python9213

• arief25ramadhan/sound-source-localization

  Four neural network architectures to classify sound source direction

  Language:Jupyter Notebook7200

• audiofhrozen/locata_wrapper

  Python code for LOCATA Challenge

  Language:Python7203

• sabbiracoustic1006/Sound-Source-Localization-using-ConvLSTM

  ConvLSTM is used to localize sound sources from Short Time Fourier Transform of Audio

  Language:Python73

• marcenacp/masterarbeit

  Simultaneous Localization and Classification of Environmental Sounds Using Deep Learning

  Language:Jupyter Notebook6200

• CahyaPutera/Sound-Source-Localization

  Sound Localization project, originally from Arief Ramdhan's Jakarta Machine Learning Talk with some adjustment. The goal is to Build MLP and CNN networks for Sound Source Localization.

  Language:Jupyter Notebook4201

• subashtimilsina/Sound-Source-Localization

  GCC-Phat and grid search in cubical microphone array.

  Language:Jupyter Notebook4102

• Roozbeh-Bazargani/music-algorithm-for-doa-estimation

  Estimating direction of arrival (DOA) from data collected by sensor arrays using music algorithm.

  Language:Python3

• kikivanderheijden/CNN_SoundLocalization

  Convolutional neural network models of sound localization

  Language:Python220

• sayanmndl21/ssloc

  Sound Source Localization

  Language:Python2201

• alexW335/lakeator

  A python package for Acoustic Source Localisation, with GUI and QGIS integration.

  Language:Python10

• LeoBalzoni/QuaternionNN_SoundEventLocalization

  Language:Python10

• nfarnesi4/senior-design-ssl-model

  Senior Design Python code for Sound Source Localization with ML model and dataset generator

  Language:Python1100

• sape344/ENVIRONMENTAL-SOUND-CLASSIFICATION

  Environmental sound classification is the preprocessing and the classification of the sounds obtained from the everyday environment. It is a crucial method to interpret the sound signals and use them in various applications. Our dataset includes a total of 2000 samples labeled under 50 specific types. To overcome this challenge, we tried various machine learning and deep learning methods such as CNN and CRNN. I also tried to augment the amount to data to provide more data to our model obtain higher performance from our models.

  Language:Jupyter Notebook1