pianos

Classify piano sound quality by fine-tuned pre-trained CNN models.

Requirements

conda create -n py311 python=3.11 -y
conda activate py311
pip install -r requirements.txt

Usage

Maintenance

git clone [email protected]:ccmusic-database/pianos.git
cd pianos

Train

Assign a backbone(take squeezenet1_1 as an example) after --model to start training:

python train.py --model squeezenet1_1 --fullfinetune True --wce True

--fullfinetune True means full finetune, False means linear probing
--wce True means using focal loss

Supported backbones

Mirror 1	Mirror 2

Plot results

After finishing the training, use the below command to plot the latest results:

python plot.py

Results

A demo result of SqueezeNet fine-tuning:

Results	Plots
Loss curve
Training and validation accuracy
Confusion matrix

Cite

@inproceedings{zhou2023holistic,
  title        = {A Holistic Evaluation of Piano Sound Quality},
  author       = {Monan Zhou and Shangda Wu and Shaohua Ji and Zijin Li and Wei Li},
  booktitle    = {National Conference on Sound and Music Technology},
  pages        = {3-17},
  year         = {2023},
  organization = {Springer}
}