English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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.

Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation