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

Recently, deep clustering (DPCL) based speaker-independent speech separationhas drawn much attention, since it needs little speaker prior information.However, it still has much room of improvement, particularly in reverberantenvironments. If the training and test environments mismatch which is a commoncase, the embedding vectors produced by DPCL may contain much noise and manysmall variations. To deal with the problem, we propose a variant of DPCL, namedDPCL++, by applying a recent unsupervised deep learning method---multilayerbootstrap networks(MBN)---to further reduce the noise and small variations ofthe embedding vectors in an unsupervised way in the test stage, whichfascinates k-means to produce a good result. MBN builds a gradually narrowednetwork from bottom-up via a stack of k-centroids clustering ensembles, wherethe k-centroids clusterings are trained independently by random sampling andone-nearest-neighbor optimization. To further improve the robustness of DPCL++in reverberant environments, we take spatial features as part of its input.Experimental results demonstrate the effectiveness of the proposed method.

Multi-channel Speech Separation Using Deep Embedding Model with Multilayer Bootstrap Networks