https://github.com/ezorrio/genre-classification

Genre Classification using Locality Sensitive Hashing (LSH)
https://github.com/ezorrio/genre-classification

Last synced: 20 days ago
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Genre Classification using Locality Sensitive Hashing (LSH)

• Host: GitHub
• URL: https://github.com/ezorrio/genre-classification
• Owner: ezorrio
• Created: 2021-03-24T13:36:46.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-04-16T19:13:52.000Z (over 3 years ago)
• Last Synced: 2024-10-10T13:35:35.332Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 1.2 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# MMD-Assignment #1: Local Sensitive Hashing (LSH) for Item Search and Genre Classification for FMA dataset.

## Team: Emin Guliev, Justus Rass & Christian Wiskott.

### Requirements

- Python 3

- Numpy

- Matplotlib

- Jupyter

### Cloning

Github repo can be cloned from:

```bash

https://github.com/ezorrio/genre-classification.git

```

If you have data files fetched from original source, clone using following command:

```bash

GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/ezorrio/genre-classification

```

### Description

The 4 Python files should be placed in a directory with the .csv files in a folder called 'metadata'.

- FMA.py

- LSH.py

- main.py

- MusicSearch.py

- metadata/

    - features.csv

    - tracks.csv

- experiments/

- paper/ - contains LaTeX source files for paper

To run it with the hyperparameters used for the final results, simply execute the 'experiments_manual.ipynb' file

located in the experiments folder.

The experiments folder contains the ipython notebooks used to carry out the tests for validation.

The experiments/results folder contains the raw results for the test suite used for k=3, 5, 7 nearest

neighbours, as well as a condensed list of the best achieved results and the manual tests carried out

with those parameters.