aascode / audio-embedding-bias

Notebooks to investigate data set bias in audio embeddings

Bias Correction with Pre-trained Audio Embeddings

Implementation of different bias correction methods for pretrained audio embeddings proposed in the following paper:

Changhong Wang, Brian McFee, and Gaël Richard. "Transfer Learning and Bias Correction with Pre-trained Audio Embeddings". Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, 2023.

(Ps: deem in deem.py is an acronym for "debiasing embeddings")

Content

• Installation
• Datasets
• Pre-trained embeddings
• Bias correction
• Contact
• Cite

Installation

We recommend using Conda environment:

git clone https://github.com/bmcfee/embedding-bias.git
conda env create -f environment.yml
conda activate embedding-bias

Datasets

Download IRMAS and OpenMIC datasets and save in directories data/irmas and data/openmic-2018, respectively.

Pre-trained embeddings

Extract VGGish, OpenL3, and YAMNet embeddings for both datasets. Or use our extracted pre-trained embeddings directly.

Bias correction

Run the note books in notebooks:

• 0_data_distribution.ipynb: investigate the distribution of each dataset in terms of genre distribution and number of samples per class
• 1_debias_linear.ipynb: linear bias correction (original, LDA, mLDA)
• 2_debias_nonlinear.ipynb: nonlinear bias correction (K, KLDA, mKLDA)
• 3_cosine_similarity.ipynb: calculate cosine similarity between dataset separation and instrument classification; check matrix rank for the case of multiple bias correction
• 4_result_summary.ipynb: summarize results from all bias correction methods

Contact

For any questions, support, or inquiries, please feel free to contact [e-mail].

Cite

Please cite the following paper if you use the code provided in this repository.

Changhong Wang, Brian McFee, and Gaël Richard. "Transfer Learning and Bias Correction with Pre-trained Audio Embeddings". Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, 2023.

@inproceedings{wang2023bias,
    author = {Changhong Wang and Brian McFee and Gaël Richard},
    title = {Transfer Learning and Bias Correction with Pre-trained Audio Embeddings},
    booktitle = {Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference},
    year = 2023,
}