Sudo RM -RF: Efficient Networks for Universal Audio Source Separation

In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRM-RF) as well as their aggregation which i...

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Bibliographic Details
Published in2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) pp. 1 - 6
Main Authors Tzinis, Efthymios, Wang, Zhepei, Smaragdis, Paris
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2020
Subjects
Audio source separation
deep learning
low-cost neural networks
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Summary:In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRM-RF) as well as their aggregation which is performed through simple one-dimensional convolutions. In this way, we are able to obtain high quality audio source separation with limited number of floating point operations, memory requirements, number of parameters and latency. Our experiments on both speech and environmental sound separation datasets show that SuDoRM - RF performs comparably and even surpasses various state-of-the-art approaches with significantly higher computational resource requirements.
DOI:10.1109/MLSP49062.2020.9231900