IliaZenkov/sklearn-audio-classification
An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP
This project helps developers understand how to classify audio data, specifically for recognizing emotions from speech. It takes raw audio snippets and, through various signal processing and machine learning techniques, outputs a prediction of the emotion conveyed. Data scientists, machine learning engineers, and researchers working with audio will find this useful for learning foundational concepts.
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Use this if you are a developer looking for an educational resource to learn the fundamentals of audio feature engineering and classification with classical ML models and MLPs.
Not ideal if you need an out-of-the-box, production-ready solution for state-of-the-art audio classification or complex deep neural networks beyond an MLP.
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Jupyter Notebook
License
MIT
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Last pushed
Nov 05, 2020
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