sustain-pedal-detection

Companion codes for the submissions:

Beici Liang, György Fazekas, Mark Sandler. "Piano Sustain-Pedal Detection Using Convolutional Neural Networks" published in ICASSP 2019 and "Transfer Learning for Piano Sustain-Pedal Detection" published in IJCNN 2019.

Index

• 0. pedal midi info.ipynb: understand MIDI files and how the ground-truth annotations are extracted

• 1.1 dataset preparation.ipynb and 1.2 sub-dataset preparation.ipynb: how to build the dataset and generate excerpts

Works related to the ICASSP paper

• 2.1 pedal onset classification.ipynb: how to train Conv2D-onset and save the model with the highest AUC in ./save-model/onset_multi_kernel.

• 2.2 pedal segment classification.ipynb: how to train Conv2D-segment and save the model with the highest AUC in ./save-model/segment_multi_kernel.

• 2.3 how mfcc performs on the small dataset.ipynb: compare with SVM using MFCC features. Performance on detecting pedal onset and pedalled segment are saved in ./save-result/small-onset_mfcc_svc_performance.npz and ./save-result/small-onset_mfcc_svc_performance.npz, respectively.

• 3. piece-wise detection.ipynb: how to fuse the decision outputs from Conv2D-onset and Conv2D-segment so as to perform the detection on a piano piece. Evaluation results are saved in ./save-result/psegment-testresult_onset98_seg98.csv.

Works related to the IJCNN paper and Chapter 7 in Beici's PhD Thesis

• 4. effect of cnn settings.ipynb: how different configurations affect the model performance. After this, best model for detecting pedal onset and pedalled segment are saved in ./save-model/sub-onset_cnnkernel-melspectrogram_l4c13 and ./save-model/sub-segment_cnnkernel-melspectrogram_multift, respectively.

• 5.1 test on chopin audio data (retrain last layer).ipynb: how to fine-tune the model trained using synthesised data to be used in the detection on real acoustic recordings.

• 5.2 test on chopin audio data.ipynb: how to do the detection even better using our proposed transfer learning method.

• visualise layers.ipynb: visualise what have been learned in the neural networks using deconvolution.

Requirements

Codes are based on the following settings and their corresponding versions.

Setting	Version
OS	Centos 7.3
GPU	Titan Xp
module	cuda/8.0-cudnn5.1
Python	2.7.5

Python dependencies can be installed by

pip install -r requirements.txt

You need to install Jupyter Notebook to run .ipynb in your local browser.