Problem
Research questions and friction points this paper is trying to address.
Band-split RNN
Music Source Separation
Reproducible Research
Energy Cost
Investigating the Performance and Energy Costs of Replicating Band-Split RNN for Music Source Separation
🤖 AI Summary
研究通过实现全管道复制并优化BSRNN模型，解决了音乐源分离中的性能和能耗问题。
📝 Abstract
Band-split recurrent neural network (BSRNN) is a popular music source separation model that yields close to state-of-the-art results using reasonable computational resources and public datasets. It is therefore interesting from a reproducible research perspective, but achieving its performance is not straightforward since its full code is not available. In this paper, we conduct a replication of BSRNN via implementing the full pipeline. We extend the original paper's analysis by experimentally studying various design choices about data preprocessing, the optimization protocol, and architectural parameters. We report and discuss this project's energy cost, and we underline how its footprint could have been substantial lower upon availability of the full pipeline, which advocates for more reproducible research practices. To comply with this objective, we publicly release our code and pre-trained models.
Innovation
Methods, ideas, or system contributions that make the work stand out.
reproducible research
energy cost
pre-trained models
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