audio-classification | Environmental sound classification using Deep | Machine Learning library

by mtobeiyf Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | audio-classification Summary

audio-classification is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. audio-classification has no vulnerabilities and it has low support. However audio-classification has 2 bugs and it build file is not available. You can download it from GitHub.

:musical_score: Environmental sound classification using Deep Learning with extracted features

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Quality

Security

License

Reuse

Support

audio-classification has a low active ecosystem.

It has 126 star(s) with 51 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 20 have been closed. On average issues are closed in 33 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of audio-classification is current.

Quality

audio-classification has 2 bugs (2 blocker, 0 critical, 0 major, 0 minor) and 10 code smells.

Security

audio-classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

audio-classification code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

audio-classification does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

audio-classification releases are not available. You will need to build from source code and install.

audio-classification has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

audio-classification saves you 81 person hours of effort in developing the same functionality from scratch.

It has 208 lines of code, 9 functions and 4 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed audio-classification and discovered the below as its top functions. This is intended to give you an instant insight into audio-classification implemented functionality, and help decide if they suit your requirements.

Train features .
Extract feature from librosa features .
Real time extraction .
Extract features from an audio file .
Predict class .
extract features from a given directory
Parse and save features .

Get all kandi verified functions for this library.

audio-classification Key Features

No Key Features are available at this moment for audio-classification.

audio-classification Examples and Code Snippets

No Code Snippets are available at this moment for audio-classification.

Community Discussions

Trending Discussions on audio-classification

name 'ImageDataBunch' is not defined

QUESTION

name 'ImageDataBunch' is not defined

Asked 2019-May-07 at 18:43

I'm getting this error "name 'ImageDataBunch' is not defined" and am not able to find a solution on the Internet.

The full code can be found here: https://dzlab.github.io/jekyll/update/2018/11/13/audio-classification/.

...

ANSWER

Answered 2019-May-07 at 18:43

You just need to import the ImageDataBunch class from the fast.ai library. Here's the docs for it. Just add the following to the top of your code to import the entirety of the vision library classes:

Source https://stackoverflow.com/questions/56028380

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install audio-classification

You can download it from GitHub.
You can use audio-classification like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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