An ML algorithm to distinguish Progressive Rock music from everything else.

Run save_feature.py.

• Each subfolder in the tran_set\ has an .ods file stating the list of songs, and .mp3 files for songs.

  • Progressive_Rock_Songs\: {'songs': 142, 'others': ['prog_train.ods']}
  • Not_Progressive_Rock\Top_Of_The_Pops\: {'songs': 87, 'others': ['notprog_top_pops_train.ods']}
  • Not_Progressive_Rock\Other_Songs\: {'songs': 272, 'others': ['notprog_other_train.ods']}

• Load music files using librosa.

  • It has features of
    • Spectral
      • Chromagram
      • Mel-scaled spectrogram
      • Mel-frequency cepstral coefficients (MFCCs)
    • Rythym
      • Tempo
    • Others
  • Feature plots saved at output/

We build several baseline models based on the last years' best project, as well as several new models.

conv1d(43,64) -> conv1d(64,64) -> linear(6848,600) -> linear(600,10) -> linear(10,2)

conv1d(43,64) -> conv1d(64,128) -> linear(13696,600) -> linear(600,30) -> linear(30,4) -> linear(4,2)

conv1d(43,64) -> conv1d(64,128) -> conv1d(128,256) -> conv1d(256,512) -> linear(13312,100) -> linear(100,10) -> linear(10,2)

conv1d(43,86) -> conv1d(86,172) -> conv1d(172,344) -> linear(18232,2)

conv1d(43,64) -> conv1d(64,128) -> conv1d(128,128) -> conv1d(128,64) -> conv1d(64,32)-> conv1d(32,32) -> linear(832,2)

• O’Brien, Tim. "Musical Structure Segmentation with Convolutional Neural Networks." 17th International Society for Music Information Retrieval Conference. 2016.

conv1d(43,128) -> conv1d(128,128) -> conv1d(128,256) -> conv1d(256,256) -> conv1d(256,256) -> conv1d(256,256) -> conv1d(256,512)-> conv1d(512,10) -> linear(40,2)

• Pons, O. Nieto, M. Prockup, E. Schmidt, A. Ehmann, and X. Serra, “End-to-end learning for music audio tagging at scale,” in Intl Society for Music Inf Retrieval Conf, 2018, pp. 1–8.

conv1d(43,128) -> res1d(128,128) -> res1d(128,256) -> … -> res1d(256,512)-> conv1d(512,10) -> linear(40,2)

• Allamy, Safaa, and Alessandro Lameiras Koerich. "1D CNN architectures for music genre classification." 2021 IEEE symposium series on computational intelligence (SSCI). IEEE, 2021.

Recurrent NN

• Place saved feature json files in relative path to this repo at ../data/[feature.json]. The default paths are set in train.py as:

  non_prog_other_path = "../data/Feature_Extraction_Other.json"
  non_prog_pop_path = "../data/Feature_Extraction_Top_Pop.json"
  prog_path = "../data/Feature_Extraction_Prog.json"

• Run main.py.

• For new CNN models, just import them into main.py and add corresponding (model_name, model()) to model_dict in main.py.

• Feature plots are saved in output folder as .pdf.

• Model results are saved in output/model folder named after the model name. These include

  • Confusion matrices for train/test snippets/songs (all in one file)
  • Average confusion matrix after multiple runs
  • Model test labeling result
  • Model pickle file

• Log file is generated at output/log_file.log

• Increasing the cutoff value of binary classification in the last layer of neural network
• Increasing the cutoff value of the proportion of snippets to become a Prog Rock song

• We try 8 different models (CNN Structure, Recurrent Structure, and ResNet structure) with two types of snippets (non-overlap and 50% overlap).
• ResNet structure with normal snippets provides best prediction accuracy.
• With post-processing techniques, we can further improve the model accuracy to 82.64%, which has 3.5% improvement compared to the baseline model.
• We still find some type of musics that are hard to be classified under our criterion.
• For future work, we suggest:
  1. More advanced post-processing classification techniques;
  2. More advanced image classification techniques Neural ODEs (Cui et al. 2023);
  3. Extract other features like lyrics.