As a part of this project, I developed an application in Python aimed at automatically classifying different musical genres from audio snippets. The challenge involved analyzing large datasets of audio files, identifying features within the music files for classification, and implementing machine learning algorithms for accurate genre prediction. This project aimed to explore the intricacies of sound data, visualize audio features, and apply machine learning techniques to classify music genres.
- Researched and selected a suitable library for processing music files.
- Implemented data preprocessing techniques for cleaning and preparing the audio data.
- Developed machine learning models, including a multi-variable linear regression model, for genre classification.
- Analyzed model performance and evaluated implications for real-life users.
- Presented findings and insights through a comprehensive presentation, detailing classification methods, assumptions, and implications.
- Python programming language for application development.
- Machine learning libraries such as TensorFlow and scikit-learn for model implementation.
- Integrated data visualization techniques to gain insights into audio features.
- Analyzed model implications and assessed risks to business impact.
This project showcased expertise in data analysis, machine learning, and communication skills, contributing to enhanced understanding and classification of music genres.