Music-Genre-Classification | Classify music in two categories progressive rock
kandi X-RAY | Music-Genre-Classification Summary
kandi X-RAY | Music-Genre-Classification Summary
Music-Genre-Classification is a Python library. Music-Genre-Classification has no bugs, it has no vulnerabilities and it has low support. However Music-Genre-Classification build file is not available. You can download it from GitHub.
Classify music in two categories progressive rock and non-progressive rock using mfcc features, MLP, and CNN.
Classify music in two categories progressive rock and non-progressive rock using mfcc features, MLP, and CNN.
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Music-Genre-Classification has a low active ecosystem.
It has 0 star(s) with 1 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
Music-Genre-Classification has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Music-Genre-Classification is current.
Quality
Music-Genre-Classification has 0 bugs and 0 code smells.
Security
Music-Genre-Classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Music-Genre-Classification code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Music-Genre-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.
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Music-Genre-Classification releases are not available. You will need to build from source code and install.
Music-Genre-Classification has no build file. You will be need to create the build yourself to build the component from source.
It has 1101 lines of code, 10 functions and 9 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Music-Genre-Classification and discovered the below as its top functions. This is intended to give you an instant insight into Music-Genre-Classification implemented functionality, and help decide if they suit your requirements.
- split the data into two arrays
- Calculates the majority probability for the given indices .
- Return a list of filenames .
- Get the list of all prog files .
- Return a list of non - prog files .
Get all kandi verified functions for this library.
Music-Genre-Classification Key Features
No Key Features are available at this moment for Music-Genre-Classification.
Music-Genre-Classification Examples and Code Snippets
No Code Snippets are available at this moment for Music-Genre-Classification.
Community Discussions
Trending Discussions on Music-Genre-Classification
QUESTION
Error in TensorFlow/Keras: ValueError: No gradients provided for any variable
Asked 2020-Oct-31 at 17:45
I am new to the implementation of deep learning and I am trying to implement a method of music genre classification with Keras / Tensorflow based on the following Git.
With some modifications to the model.
The code I am implementing is as follows:
...ANSWER
Answered 2020-Oct-31 at 17:45Add loss:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Music-Genre-Classification
You can download it from GitHub.
You can use Music-Genre-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.
You can use Music-Genre-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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