anasvaf

anasvaf's Stars

• home-assistant/core

  :house_with_garden: Open source home automation that puts local control and privacy first.

  Language:Python73.9k1.3k54.1k31k

• gotev/android-upload-service

  Easily upload files (Multipart/Binary/FTP out of the box) in the background with progress notification. Support for persistent upload requests, customizations and custom plugins.

  Language:Kotlin2.8k101576693

• fgadaleta/deeplearning-ahem-detector

  Language:Jupyter Notebook43921145

• jaron/deep-listening

  Deep Learning experiments for audio classification

  Language:Jupyter Notebook1499441

• MarvinBertin/HiddenMarkovModel_TensorFlow

  TensorFlow: Viterbi, Forward-Backward and Baum Welch with a Hidden Markov Model (HMM)

  Language:Jupyter Notebook13318250

• nilmtk/nilmtk-contrib

  Language:Python116106860

• ebouteillon/freesound-audio-tagging-2019

  Freesound Audio Tagging 2019

  Language:Jupyter Notebook953215

• karolpiczak/paper-2015-esc-convnet

  Environmental Sound Classification with Convolutional Neural Networks - paper replication data

  Language:Jupyter Notebook7541027

• maxfrenzel/SpectrogramVAE

  TensorFlow implementation of a VAE for encoding spectrograms

  Language:Jupyter Notebook674113

• jordipons/neural-classifiers-with-few-audio

  Training neural audio classifiers with few data − https://arxiv.org/abs/1810.10274

  Language:Python607312

• SeeedDocument/ReSpeaker-4-Mic-Array-for-Raspberry-Pi

  5615025

• ajhalthor/audio-classifier-convNet

  Language:Jupyter Notebook466226

• toni-heittola/icassp2019-tutorial

  ICASSP2019 Tutorial: Detection and Classification of Acoustic Scenes and Events / Code examples

  Language:Jupyter Notebook411021

• maechler/nnilm

  A reimplementation of Jack Kelly's rectangles neural network architecture based on Keras and the NILMToolkit.

  Language:Python36366

• LeadingIndiaAI/Wake-UP-word-detection

  Wake-up-word(WUW)system is an emerging development in recent times. Voice interaction with systems have made life ease and aids in multi-tasking. Apple, Google, Microsoft, Amazon have developed a custom wake-word engine, which are addressed by words such as ‘Hey Siri’. ‘Ok Google’, ‘Cortana’, ‘Alexa’. Our project focuses initially only detection and response to a customized wake-up command. The wake-up command used is “GOLUMOLU”. A wake-up-word detection system search for specific word and reads the word, where it rejects all other words, phrases and sounds. WUW system needs only less memory space, low computational cost and high precision. Artificial Neural Networks(ANN) have reduced the complexity, computational time, latency, thus the efficiency of system has improved. Deep learning has improved the efficiency of automatic speech recognition(SR), where wake word detection is a subset of SR but unlike keyword spotting and voice recognition. A deep learning RNN model is used for the training of the network. RNN are specifically used in case of temporal sequence data and has the ability to process data of different length but of same dimension. For training a model, labelled dataset is needed. We generated three forms of data: golumolu, negative and background. Such that, the model learns circumspectly and attentively detects when specific word found. To start communication with system, the wake word should be delivered. The main task of WUW detection system is to detect the speech, to identify WUW words among spoken words, to check whether the word spoken in altering context.

  Language:Jupyter Notebook17119

• Mayank-Bhatia/UrbanSound_Classification

  Sound classification using neural networks

  Language:Jupyter Notebook12312

• thayermldac/vae

  A Playground for Variational Autoencoders

  Language:Jupyter Notebook12202

• deeplearningzhy/DL

  TensorFlow，DCGAN，VAE，LSTM，CNN，Acoustic Scene Classification

  Language:Python11100

• tuwien-musicir/mir_lecture

  Language:Jupyter Notebook10304

• TUT-ARG/CASSE_book_ch2_examples

  Language:Python8411

• seven-up-purdue/uav-audio-detection

  Language:Jupyter Notebook6318

• Fesche/NILM

  Exploring the UK-dale data set

  Language:Jupyter Notebook5111

• dangpzanco/dcase_tutorials

  A bunch of dcase_util (https://dcase-repo.github.io/dcase_util/) tutorials in Python notebooks.

  Language:Jupyter Notebook4201

• nastaran75/acoustic-scene-classification-JKU

  My implementations during my internship at CP-JKU

  Language:Python30

• changfangyi/energydisagg

  Language:Jupyter Notebook200

• lcances/DCASE2018_task4

  Language:Python2100

• lgotarra/Research-Audio-classification-using-Audioset-Freesound-Databases

  Final degree project based on task 2 from DCASE Challenge 2018. The goal is understanding and comparing different audio tagging systems that use deep learning techniques. Based on Tensorflow library. Proposal for improvement (not completed)

  Language:Python21

• n-getty/nilm

  Language:Python21

• sambaiga/NILM

  Code for NILM research activities

  Language:Jupyter Notebook2200

• svarmit/MachineLearning-on-Sound-data

  This repository consists of files and the machine learning ipython notebook demonstrating the machine learning techniques to identify the source of audio

  Language:Jupyter Notebook2101