Over the past few years, streaming services with massive catalogs have become the primary means most people listen to their favorite music. But at the same time, the sheer amount of music on offer can mean users might be a bit overwhelmed when trying to look for newer music that suits their tastes. For this reason, streaming services have looked into means of categorizing music to allow for personalized recommendations. One method involves direct analysis of the raw audio information in a given song, scoring the raw data on various metrics. We'll be examining data compiled by a research group known as The Echo Nest. Our goal is to look through this dataset and classify songs as being either 'Hip-Hop' or 'Rock' - all without listening to a single one ourselves.
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Used machine learning models like XGBoost, Random Forest, and Logistic Regression to classify songs from The Echo Nest dataset as 'Hip-Hop' or 'Rock' without audio playback. XGBoost had the best accuracy, with plans to refine hyperparameters for improved results.
ashraf711/Song-Genre-classification-using-audio-data
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Used machine learning models like XGBoost, Random Forest, and Logistic Regression to classify songs from The Echo Nest dataset as 'Hip-Hop' or 'Rock' without audio playback. XGBoost had the best accuracy, with plans to refine hyperparameters for improved results.
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