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https://github.com/alpha597/music_classification_ml
A project which compares different machine learning algorithms' accuracy in music genre classification of a large dataset.
https://github.com/alpha597/music_classification_ml
machine-learning pandas python scikit-learn tensorflow
Last synced: 3 days ago
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A project which compares different machine learning algorithms' accuracy in music genre classification of a large dataset.
- Host: GitHub
- URL: https://github.com/alpha597/music_classification_ml
- Owner: alpha597
- Created: 2025-02-15T16:09:08.000Z (5 days ago)
- Default Branch: main
- Last Pushed: 2025-02-15T16:56:48.000Z (5 days ago)
- Last Synced: 2025-02-15T17:30:29.416Z (5 days ago)
- Topics: machine-learning, pandas, python, scikit-learn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 166 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Music_Classification_ML
Classify music dataset using Machine Learning algorithms using Tensorflow and Skicit-learnthis classification is done by several features of music- tempo, loudness, beats, pitch, root mean square error, spectral_centroid, zero_crossing rate etc.
Extra features extracted from audio files, using librosa library of Python
Classifying music into 10 generes - Classical , blues, rock, country, metal , pop, jazz etc. Each music genre has it's own feature combination and we want to classify the dataset into 10 groups. At first we have used the Support Vector machine(SVM) algorithm both linear and rbf modelCollected music dataset from https://www.kaggle.com/datasets/insiyeah/musicfeatures/data
below is accuracy of this model -confusion matrix
SVM model accuracy - approx 60%
next algorithm to apply - KNN, K-Means Clustering, Neural networks... etc
SVM performance can be increased using unsupervised learning PCA algorithm