Try it out: https://soundseg.com/

SoundSeg

Introduction

SoundSeg is an advanced audio processing project, focused on segmenting and analyzing sound data. The core of the project revolves around machine learning models, specifically designed for audio source seperation of songs into the vocal component.

Please see the projects preprint for more details: Spectral Mapping of Singing Voices: U-Net-Assisted Vocal Segmentation

Example Audio and Spectrograms

Original Audio

• Mixture: https://drive.google.com/file/d/1_o-BfOWwSTL52qE1aadqPhRNH2Top-gA/view?usp=sharing

Predicted Output

• Vocal (Predicted): https://drive.google.com/file/d/1_ncbyUDC5GpL0-yJN7bObyk7KMjnQGuy/view?usp=sharing

Spectrograms

• Mixture:
  
• Vocal (Predicted):
  

System Model

Contents

• MUSDB_README.md: Provides details on the MUSDB 2018 Dataset, crucial for training and testing the models.
• demo.ipynb: A Jupyter notebook demonstrating the capabilities of SoundSeg.
• src: Contains the source code of the project.
• Scripts: Shell scripts for preprocessing (0_preprocess.sh), training (1_train.sh), and evaluation (2_eval.sh).
• Python Modules: Core modules like model.py, preprocessing.py, train.py, evaluate.py, etc., for model development and data handling.
• requirements.txt: Lists all the necessary dependencies.
• analysis.ipynb: Additional Jupyter notebook for deeper analysis.

Usage

1. Preprocessing: Run the 0_preprocess.sh script to prepare your data.
2. Training: Execute the 1_train.sh script to train the models.
3. Evaluation: Use the 2_eval.sh script for model evaluation.
4. Demo: Explore demo.ipynb for hands-on examples and usage demonstrations.

5. Analysis: Delve into analysis.ipynb for in-depth analytical insights.

Model Weights

Download the pre-trained model weights from the following links:

• Google Drive Link [https://drive.google.com/file/d/1_myj2HVAg-g6SR44jSRB9tIkv7BhF1gU/view?usp=sharing]

Results

SDR	SIR	SAR	Normalization	Scaler	Loss
7.1	25.2	7.2	frequency	Min/Max	MAE
7.1	25.1	7.2	time	Min/Max	MAE
6.7	24.8	6.8	frequency	Min/Max	MSE
5.7	23.9	5.8	time	Quantile	MAE
5.6	23.3	5.7	time	Min/Max	MSE
4.8	22.6	4.9	time	Quantile	MSE
-0.9	16.6	-0.6	frequency	Quantile	MSE
-2.1	15.8	-1.8	frequency	Quantile	MAE

Acknowledgments

Special thanks to the creators of the MUSDB 2018 Dataset and all contributors to this project.