rock or not?

Tunçer D, Barçin K, Bölücü N, Erdem A. Music Genre Recognition with Machine Learning. Poster presented at 27th Signal Processing and Communications Applications Conference (SIU), Sivas, 2019.

In order to run

• Support Vector Machines support_vector_machines.py or support_vector_machines.ipynb
• Nearest Neighbors nearest_neighbors.py or nearest_neighbors.ipynb
• Logistic Regression logistic_regression.py or logistic_regression.ipynb
• Neural Network nn.py or nn.ipynb

extract tracks.csv and features.csv from FMA: A Dataset For Music Analysis

You can test your audio on: application.ipynb

• Video Presentation: rock or not?
• Project Presentation: rock_or_not_presentation.pptx
• Progress Report: progress_report.pdf
• Final Report: final_report.pdf

Medium Blogs:

• Week 1 Introduction
• Week 2 Dataset Exploration
• Week 3 Dataset Exploration (Continued)
• Week 4 Related Works
• Week 5 Methods
• Week 6 Methods (Continued)
• Week 7 Results and Conclusion

Course Page: BBM406-Fall2018

REFERENCES

FMA: A Dataset for Music Analysis