• model architecture: simple convolutional neural network

• prediction: probability of onset

This repository is implementation of Jan Schlüter and Sebastian Böck's "IMPROVED MUSICAL ONSET DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS"

ABSTRACT:

Musical onset detection is one of the most elementary tasks in music analysis, but still only solved imperfectly for polyphonic music signals. Interpreted as a computer vision problem in spectrograms, Convolutional Neural Networks (CNNs) seem to be an ideal fit. On a dataset of about 100 minutes of music with 26k annotated onsets, we show that CNNs outperform the previous state-of-the-art while requiring less manual preprocessing. Investigating their inner workings, we find two key advantages over hand-designed methods: Using separate detectors for percussive and harmonic onsets, and combining results from many minor variations of the same scheme. The results suggest that even for well-understood signal processing tasks, machine learning can be superior to knowledge engineering.

Using PyTorch, implemented musical onset detectioner and apply it to Taikosanjiro which is sounds game like taiko-no-tatsujin

• git-lfs (for downloading large dataset)
• python3
• pytorch
• soundfile
• librosa
• tqdm

you can download from here

• install requirement

$ pip install -r reqirement.txt

• before training, prepare audio dataset. (and pickle data.)

$ python music_processor.py reduced
$ python music_processor.py test

• then, train model.

$ python train.py don
$ python train.py ka

• then predict onset probability with trained model.

$ python infer.py don
$ python infer.py ka

• and create notes and corresponding music from inferred data.

$ python synthesyze.py 

• train_reduced in this repo is too small for training, because of limitation of uploadable file size of git, I wasn't able to upload enough size training data.

• if you want to train model with larger data, you could download audio data and corresponding notes here.

• IMPROVED MUSICAL ONSET DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS
• Dance Dance Convolution
• Create with AI
• Tom Mullaney's Article; Rythm Games with Neural Networks
• Woody's Article; Musical Data Processing with CNN