Abstract
Monaural singing voice separation (MSVS) is a challenging task and has been extensively studied. Deep neural networks (DNNs) are current state-of-the-art methods for MSVS. However, they are often designed manually, which is time-consuming and error-prone. They are also pre-defined, thus cannot adapt their structures to the training data. To address these issues, we first designed a multi-resolution convolutional neural network (CNN) for MSVS called multi-resolution pooling CNN (MRP-CNN), which uses various-sized pooling operators to extract multi-resolution features. We then introduced Neural Architecture Search (NAS) to extend the MRP-CNN to the evolving MRP-CNN (E-MRP-CNN) to automatically search for effective MRP-CNN structures using genetic algorithms optimized in terms of a single objective taking into account only separation performance and multiple objectives taking into account both separation performance and model complexity. The E-MRP-CNN using the multi-objective algorithm gives a set of Pareto-optimal solutions, each providing a trade-off between separation performance and model complexity. Evaluations on the MIR-1 K, DSD100, and MUSDB18 datasets were used to demonstrate the advantages of the E-MRP-CNN over several recent baselines.