Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)
Published in 2021 AAAI Conference on Artificial Intelligence, 2021
Recommended citation: Gabriel Mersy and Jin Hong Kuan. 2021. Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence, 35, 18 (May 2021), 15847-15848. https://ojs.aaai.org/index.php/AAAI/article/view/17920
BibTeX citation:
@article{Mersy_Kuan_2021, title={Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17920}, abstractNote={Given recent advances in deep music source separation, we propose a feature representation method that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). We train a depthwise separable convolutional neural network on a challenging electronic dance music (EDM) data set and compare its performance to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting compared to the standard single spectrogram approach.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Mersy, Gabriel and Kuan, Jin Hong}, year={2021}, month={May}, pages={15847-15848} }