Prototype EnCodecMAE (PECMAE)

This site contains sonification of the prototypes obtained with PECMAE models. Aditionally, we provide baselines consisting of a random sample from the training set of each dataset and sonifacions obtained with an APNet model.

Updates

• ⚠️ 2024/02/02. Fixed prototype URLs in GTZAN

  By mistake we duplicated the links for prototypes 0 and 1, and they sounded identical. Fixed now

• 2024/01/22. Newer PECMAE model with 10-second context

  The sonification results obtained with model PECMAE-5 (10s) are based on a newer autoencoder featuring the same architecture presented in the paper but using a longer context window of 10 seconds. Results suggest that extending the receptive field allows to achieve prototypes that better resemble their classes.

Citation

If results, insights, or code developed within this project are useful for you, please consider citing our work:

@inproceedings{alonso2024leveraging,
  author    = "Alonso-Jim\'{e}nez, Pablo and Pepino, Leonardo and Batlle-Roca, Roser and Zinemanas, Pablo and Bogdanov, Dmitry and Serra, Xavier and Rocamora, Mart\'{i}n",
  title     = "Leveraging Pre-trained Autoencoders for Interpretable Prototype Learning of Music Audio",
  maintitle = "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
  booktitle = "ICASSP Workshop on Explainable AI for Speech and Audio (XAI-SA)",
  year      = 2024,
}

Acknowledgments

This work has been supported by the Musical AI project - PID2019-111403GB-I00/AEI/10.13039/501100011033, funded by the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación.