This project is a demo of how modern bio-inspired spiking neural network(SNN) can be integrated into music information retrieval(MIR) applications. The project incorporates two popular SNN simulator: Brian2 and NEST to demonstrate SNN can detect onsets of notes in a music clip. The author is Alex Huang-Yu Yao, please contact [email protected].

• Python3
• Numpy
• Brian2
• Brian2hears
• NEST
• ODB dataset
• Optional(for additional visualization) -- Matplotlib

• gcc compiler
• tee

• SNNinMIR.pdf is the detailed technical description file of the project.
• run.sh is the 1-step easy-running script.
• main.py is the core program for onset detection.
• network.py is an SNN only simulation routine without music input for network dynamics evaluation.
• LICENSE: the project is under MPLv2.
• README.md is what you are reading.
• mir-term.odp is the in-class presentation slides on 2019/06/18. Actually, all the contens are included in SNNinMIR.pdf.

1. Clone or download this repository and download the ODB dataset.
2. Switch to the top level of this repository, i.e. cd /path/to/SNN-in-MIR.
3. Create a dirctory to put dataset in. mkdir Datasets and move all the file below the ODB and evaluator directories under Datasets. It should look like this: SNN-in-MIR/Datasets/[ODB/evaluator]/....
4. Execute run.sh to predict all onset points and obtain the evaluation results in the ODB dataset. The results will be saved under the top-level directory. If you find that you cannot execute the script, you should try sudo chmod +x run.sh.

Note: The programs are developed and tested on Arch Linux. Non-linux users may need to modify some instructions in the source codes or operate in a virtual machine.