---
# System prepended metadata

title: '[SOTA] music classification'

---

# [SOTA] music classification

## Objectifs

- Labellisation automatique interprétable côté métier:

    - Tempo
    - Style
    - Emotion

- Data = connu, stock, compo...
- Durée courte ?

## Littérature

Mood classification, genre classification = champs de recherches connus.

**Mood classification**
- label plus subjectif
- très lié au rythme (ref. dans [1])
- Relation quasi linéaire avec mode>tempo>register>dynamics>articulation>timbre [2]
- 0.7 AUC

**Genre classification**
- most widely studied area.
- 0.9 accuracy

## Méthodes

### Feature-engineering

Historique. Familles : timbre, temporal, rythm, pitch, harmony [1].
Utiliser librosa.
Features acoustic VS features visual (sur spectrogram). Visual>acoustic? [4]
SVM donne les meilleurs résultats en classif.

### Deep learning

Utilisent Mel-spectrograms.

- ![](https://i.imgur.com/WcFzPua.png)

Choix pas si crucial.

#### 1D vs 2D

- Musique = invariance domaine du temps > invariance domaine des fréquences.
- Archi 2D beaucoup plus lourdes (poids et calculs) [5].
- 2D légèrement au dessus de 1D sur les perfs si data et hardware [5].

#### Domaine: temps/fréquence ou temps
Sur le temps : SampleCNN [6]. Pas intéressant en terme de perfs.


#### Techniques avancées/tricks
- On peut downscaler à 8kHz [6], 3s/6s de signal en général [5].
- On peut faire du transfer [6] (+ ref. dans [5])
- "Squeeze-and-excitation" ? (ref. dans [5])
- "attention mechanism" a bien marché sur mood (ref. dans [5])
- residual connections
- data augmentation => muda.readthedocs.io
- hand-crafted features + learned features? [4] 

## Databases
- MTAT => 26k, genre, mood, instrument etc. Style des musiques est apparemment pas très représentatif. Problème pour transfer ?
- MSD => 1M, "song level tags", genre + last.fm tag annotation. 200K tags (mood?).
- FMA => 100k. Genre principalement, tags. No mood?
- https://artlist.io/ => mood, video theme, genre, instrument. Scraping ? Manuel ?

## Pistes choisies

1) FE+ML [7][8][9]
2) CNN [10]

## Références
[1] Fu, Z., Lu, G., Ting, K. M., & Zhang, D. (2010). A survey of audio-based music classification and annotation. IEEE transactions on multimedia, 13(2), 303-319.
[2] Eerola, T., Friberg, A., & Bresin, R. (2013). Emotional expression in music: contribution, linearity, and additivity of primary musical cues. Frontiers in psychology, 4, 487.
[3] Huang, Y. S., Chou, S. Y., & Yang, Y. H. (2017, December). Music thumbnailing via neural attention modeling of music emotion. In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 347-350). IEEE.
[4] Costa, Y. M., Oliveira, L. S., & Silla Jr, C. N. (2017). An evaluation of convolutional neural networks for music classification using spectrograms. Applied soft computing, 52, 28-38.
[5] Nam, J., Choi, K., Lee, J., Chou, S. Y., & Yang, Y. H. (2018). Deep learning for audio-based music classification and tagging: Teaching computers to distinguish rock from bach. IEEE signal processing magazine, 36(1), 41-51.
[6] Lee, J., Park, J., Kim, K. L., & Nam, J. (2018). Samplecnn: End-to-end deep convolutional neural networks using very small filters for music classification. Applied Sciences, 8(1), 150.
[7] Sharma, S., Fulzele, P., & Sreedevi, I. (2018, April). Novel hybrid model for music genre classification based on support vector machine. In 2018 IEEE symposium on computer applications & industrial electronics (ISCAIE) (pp. 395-400). IEEE.
[8] Bahuleyan, H. (2018). Music genre classification using machine learning techniques. arXiv preprint arXiv:1804.01149.
[9] Elbir, A., Çam, H. B., Iyican, M. E., Öztürk, B., & Aydin, N. (2018, October). Music Genre Classification and Recommendation by Using Machine Learning Techniques. In 2018 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-5). IEEE.
[10] Choi, K., Fazekas, G., & Sandler, M. (2016). Automatic tagging using deep convolutional neural networks. arXiv preprint arXiv:1606.00298.