TONet
Introduction
The official implementation of "TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music", in ICASSP 2022
We propose TONet, a plug-and-play model that improves both tone and octave perceptions by leveraging a novel input representation and a novel network architecture. Any CFP-input-based Model can be settled in TONet and lead to possible better performance.
Main Results on Extraction Performance
Experiments are done to verify the capability of TONet with various baseline backbone models. Our results show that tone-octave fusion with Tone-CFP can significantly improve the singing voice extraction performance across various datasets -- with substantial gains in octave and tone accuracy.
Getting Started
Download Datasets
After downloading the data, use the txt files in the data folder, and process the CFP feature by feature_extraction.py.
Overwrite the Configuration
The config.py contains all configurations you need to change and set.
Train and Evaluation
python main.py trainpython main.py testProduce the Estimation Digram
Uncomment the write prediction in tonet.py
Model Checkpoints
We provide the best TO-FTANet checkpoints in this link. More checkpoints will be uploaded.
Citing
@inproceedings{tonet-ke2022, author = {Ke Chen, Shuai Yu, Cheng-i Wang, Wei Li, Taylor Berg-Kirkpatrick, Shlomo Dubnov}, title = {TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music}, booktitle = {{ICASSP} 2022}}
