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Proposed a system which classifies animal sound using a deep convolutional neural network. This repo contains animal sounds used in this work.
Area : Combination of machine learning and embedded hardware design Tools used: Raspberry Pi, Microphone, numpy, Scipy, Wi-fi Module We (team of 4) developed a prototype of a embedded application which could be fitted on a safari vehicle. When the safari goes in the jungle for a ride, if it detects sound, it takes a short sample and tries to classify it according to a pre-trained prediction model. The machine learning algorithm used was random forest. We were successfully able to train the model on lion , tiger, peocock, wolf , elephant and several more such wild animals. The sound samples were obtained from several animal sound repositories. The model achieved an accuracy of around 87% on the test data surmounting problems like noise in sound files, lack of extensive training examples. We also used a Wi-fi module so that information about the animal detected can be broadcast to the tourists' mobile devices.
Kaggle | 1st place solution for Freesound Audio Tagging 2019
Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".
Different VAD algorithms using Speech features
:musical_score: Environmental sound classification using Deep Learning with extracted features
Binary classification of audio recordings: sounds of rain and animals. The notebook "start" contains primary data visualization, Fourier transforms and converting sound files to their spectrogram. Two neural network architectures are implemented: 1d - CNN and 2d - CNN. The framework used is tensorflow.
A chatroom built with Flask, featured with Markdown support and code syntax highlight.
The main goal of this project was to build an Artificial Neural Network model with limited amount of sound data of various endangered animal species. The model can be further improved and can be used to located certain animal species in the wild.
Code for Yun Wang's PhD Thesis: Polyphonic Sound Event Detection with Weak Labeling
Convolutional neural network classifier for vocal tract diseases
2018秋哈工大视听觉实验
UrbanSound classification using Convolutional Recurrent Networks in PyTorch
code for our 1st prize system to win the DCASE2017 task4 challenge
Baseline of dcase 2019 task 4
Dcase2020_Task5
Repo associated to the DESED dataset, download and creation of data
This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS) Challenge.
Environmental sound classification with Convolutional neural networks and the UrbanSound8K dataset.
Cross-platform, customizable ML solutions for live and streaming media.
Robust Speech Activity Detection (SAD) in movie audio
Models and examples built with TensorFlow
The Audio Set Ontology aims to provide a comprehensive set of categories to describe sound events.
Paderborn Sound Event Detection
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.