This project focuses on audio-sample analysis, offering a comprehensive solution for music enthusiasts and researchers. The classifier employs Matplotlib for frequency-based data visualization, utilizes data cleaning and pre-processing techniques, and employs cross-validation to select the most effective machine learning model.
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Data Preparation:
- Ensure your audio samples are in a compatible format.
- Update the file paths in the designated section for seamless data loading.
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Data Visualization:
- Leverage Matplotlib plots to gain insights into the frequency-based domain of the audio data.
- Use Pandas for creating the dataframe.
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Data Processing:
- Apply data cleaning and pre-processing steps for optimal feature extraction (for more details on the procedure, refer to code).
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Model Selection:
- Perform cross-validation score checks and identify the best-performing machine learning model.
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Chord Classification:
- Implement the chosen model to classify chords in audio samples.
- Python 3.8
- Os
- Matplotlib
- Pandas
- Numpy
- Seaborn
- Scipy
- Sklearn
Feel free to contribute by forking the repository and submitting pull requests. Bug reports, feature requests, and feedback are welcome.
Refer to the ".ipynb" file for the procedure and methodology involved.