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https://github.com/brucewlee/music-genre-classification

Straightforward starter code for music genre classification using: LSTM-RNN, CNN, and just plain Neural Networks.

https://github.com/brucewlee/music-genre-classification

Science Score: 23.0%

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    Low similarity (8.0%) to scientific vocabulary

Keywords

cnn deep-learning keras-tensorflow lstm music-genre-classifier
Last synced: 9 months ago · JSON representation

Repository

Straightforward starter code for music genre classification using: LSTM-RNN, CNN, and just plain Neural Networks.

Basic Info
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
cnn deep-learning keras-tensorflow lstm music-genre-classifier
Created over 5 years ago · Last pushed over 5 years ago
Metadata Files
Readme

readme.md

Music Genre Classification

LSTM-RNN, CNN, Simple NeuralNetworks

MusicGenreClassification

This repo is meant to provide simple and straightforward starter codes to those beginning a project in music Genre Classification using Deep Learning Techniques like LSTM, CNN, and just plain old-school Neural Networks. This model can classify new audio files into four categories: Latin American, Asian, Middle Eastern, and African Music.

I hope that this work can help in several Deep Learning, Machine Learning projects in Music Genre Classification. The training data isn't provided here.

Getting Started

The three main model construction/training/evaluation algorithms as below: NN, CNN, LSTM. 1. NNMusicClassification.py -> Simple Neural Network Made with TensorFlow 2. CNNMusicClassification.py -> Convolutional Neural Network Made with TensorFlow 3. LSTMMusicClassification.py -> RNN-LSTM Made with TensorFlow

Below: NNMusicClassification.py Image of SNN Below: CNNMusicClassification.py Image of SNN Below: LSTMMusicClassification.py Image of SNN

Other Files

  1. main.py -> Make predictions using saved models from running the above codes
  2. test_data -> Provided for use in main.py

Owner

  • Name: Bruce W. Lee (이웅성)
  • Login: brucewlee
  • Kind: user
  • Location: Philadelphia, PA
  • Company: University of Pennsylvania

Research Scientist - NLP

GitHub Events

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Last synced: over 1 year ago

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  • Total Commits: 23
  • Total Committers: 2
  • Avg Commits per committer: 11.5
  • Development Distribution Score (DDS): 0.13
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Bruce W. Lee w****e@g****m 20
Bruce W. Lee b****s@s****u 3
Committer Domains (Top 20 + Academic)
seas.upenn.edu: 1

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Dependencies

requirements.txt pypi
  • Keras-Preprocessing ==1.1.2
  • Markdown ==3.3.3
  • Pillow ==8.0.1
  • SoundFile ==0.10.3.post1
  • Werkzeug ==1.0.1
  • absl-py ==0.11.0
  • appdirs ==1.4.4
  • astunparse ==1.6.3
  • audioread ==2.1.9
  • cachetools ==4.1.1
  • certifi ==2020.11.8
  • cffi ==1.14.4
  • chardet ==3.0.4
  • cycler ==0.10.0
  • decorator ==4.4.2
  • gast ==0.3.3
  • google-auth ==1.23.0
  • google-auth-oauthlib ==0.4.2
  • google-pasta ==0.2.0
  • grpcio ==1.33.2
  • h5py ==2.10.0
  • idna ==2.10
  • joblib ==0.17.0
  • kiwisolver ==1.3.1
  • librosa ==0.8.0
  • llvmlite ==0.34.0
  • matplotlib ==3.3.3
  • numba ==0.51.2
  • numpy ==1.18.5
  • oauthlib ==3.1.0
  • opt-einsum ==3.3.0
  • packaging ==20.7
  • pooch ==1.3.0
  • protobuf ==3.14.0
  • pyasn1 ==0.4.8
  • pyasn1-modules ==0.2.8
  • pycparser ==2.20
  • pyparsing ==2.4.7
  • python-dateutil ==2.8.1
  • requests ==2.25.0
  • requests-oauthlib ==1.3.0
  • resampy ==0.2.2
  • rsa ==4.6
  • scikit-learn ==0.23.2
  • scipy ==1.5.4
  • six ==1.15.0
  • sklearn ==0.0
  • tensorboard ==2.4.0
  • tensorboard-plugin-wit ==1.7.0
  • tensorflow ==2.3.1
  • tensorflow-estimator ==2.3.0
  • termcolor ==1.1.0
  • threadpoolctl ==2.1.0
  • urllib3 ==1.26.2
  • wrapt ==1.12.1