PK Score
react is winner.
react is better than automatic-classification-of-environmental-sounds-with-convolutional-neural-networks-cnns-.
More interesting! Play with react's owner and automatic-classification-of-environmental-sounds-with-convolutional-neural-networks-cnns-'s owner PK.
open Graph Image
open Graph Image
description
Abstract With the advancement of Deep Neural Networks (DNN), the accuracy of sound classification such as Urban Sound Classification, Environmental Sound Classification etc., has been significantly improved. In this project, we propose a model that uses Convolutional Neural Networks (CNN) to identify sound based on the spectrograms for different sound samples collected. The model can be used for detection of deforestation, detection of shooting in urban areas and detection of strange noises at odd hours in streets such as Air Conditioner, Car Horn, Children Playing, Dog bark, Drilling, Engine Idling, Gun Shot, Jackhammer, Siren, Street Music etc., Challenges Environmental sound work has two major obstacles, namely the lack of audio data labelled. Previous work focused on audio from carefully produced films or TV tracks from particular environments such as elevators or office spaces and commercial or proprietary datasets. Lack of fundamental vocabulary in Environmental Sounds work. This means that the classification of sounds in to the semantic groups may vary from study to study, making it difficult to compare results so the goal of this notebook is to address the two challenges mentioned above. Dataset The dataset is called UrbanSound8K and contains 8732 labelled sound excerpts (<=4s) of urban sounds from 10 classes: - The dataset contains 8732 sound excerpts (<=4s) of urban sounds from 10 classes, namely: Air Conditioner Car Horn Children Playing Dog bark Drilling Engine Idling Gun Shot Jackhammer Siren Street Music The attributes of data are as follows: ID Unique ID of sound excerpt Class type of sound Problem statement It will show how to apply Deep Learning techniques to environmental recognition sounds, focusing specifically on recognizing unique Environmental sounds. If we give an audio sample of a few seconds duration in a computer-readable format (such as a.wav file), we want to be able to determine whether it contains one of the target Environmental sounds with a corresponding classification accuracy score. Note: Loading audio files and pre-processing takes some times to complete with large dataset. To avoid reload every time reset the kernel or resume works on next day, all loaded audio data will be serialized into a object file. so next round only need to load the seriazed object file. Optional GPU configuration initialization
description
The library for web and native user interfaces.
homepage
https://react.dev
url
https://githubhelp/kranthi1231/automatic-classification-of-environmental-sounds-with-convolutional-neural-networks-cnns-
url
https://githubhelp/facebook/react
owner Avatar
owner Avatar
Recommend Projects
-
-
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. 📊📈🎉
-
Recommend Topics
-
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.
-
Recommend Org
-
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.
-