/MIREX-2019

Code submissions for mirex 2019 audio train/test tasks

Primary LanguagePython

Mirex 2019 Audio Classification (Train/Test) Tasks

Tasks

Task No of samples No of Classes Results
Audio Classical Composer Identification 2772 11
Audio US Pop Music Genre Classification 7000 10
Audio Latin Music Genre Classification 3227 10
Audio Music Mood Classification 600 5 Team AS4
Audio K-POP Mood Classification 1438 5 Team AS5
Audio K-POP Genre Classification 1894 7

Requirements

  • Python >= 3.6
  • Python packages:
    • librosa, numpy, pandas, joblib, tqdm, sklearn, albumentations, runstats
    • PyTorch >= 1.1

Setting up environment

Use the provided packaged archive (created using conda-pack, and the file is available in the releases page):

  • mkdir -p mirex
  • tar -xzf mirex.tar.gz -C mirex
  • source mirex/bin/activate
  • conda-unpack

Or, create a new conda environment using the provided environment.yml file: conda create -f environment.yml

Running commands

generating sample data

python generate_sample_data.py -d data/ -i data/sample.wav

Feature extraction

python extract_features.py -s /home/scratch -i data/features_extraction.txt -n 4

Training

python train.py -s /home/scratch -i data/train.txt -n 4 -t kpop_mood

Classifying

python classify.py -s /home/scratch -i data/test.txt -o test_preds.txt -n 4 -t kpop_mood

Time taken

Features extraction

  • 4 threads ~ 5 min
  • ~ 1.5 GB memory for extracted features # maychange with parameters