DeepMusic

Deep Learning has advanced machine learning applications such as classification, forecasting, prediction, and information retrieval. Especially, deep neural networks model such as convolutional neural networks and Long Short Term Memory (LSTM) has been applied to non textual data such as images, audios, and videos. In this project, we will develop a model for music representation learning. Possible applications include similarity search, music generation.

Potential resources:

• https://actu.epfl.ch/news/artificial-musician-builds-new-melodies-without-mu/
• https://sites.google.com/view/bachprop-icml18/
• B, F. C., Seeholzer, A., & Gerstner, W. (2017). Deep Artificial Composer: A Creative Neural Network Model for Automated Melody Generation, 10198, 81–96. http://doi.org/10.1007/978-3-319-55750-2
• Typke, R., Wiering, F., & Veltkamp, R. C. (2005). A Survey Of Music Information Retrieval Systems. Ismir, 153–160. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.7958
• Wang, A. (2006). The Shazam music recognition service. Communications of the ACM, 49(8), 44. http://doi.org/10.1145/1145287.1145312
• IDI, N. (n.d.). Methods for retrieving musical information based on rythm and pitch correlations, 1–15. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128.4754&rep=rep1&type=pdf

Deliverables: datasets, codebase, trained models, accuracy results, web or mobile app (OPTIONAL)

The project requires programming skills in Python, PyTorch (or Tensorflow, etc.). Competent knowledge and experience in Deep Learning. Pro-active in learning and trying new things, everyday.