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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Nonnegative Tucker decomposition (NTD), a tensor decomposition model, hasreceived increased interest in the recent years because of its ability toblindly extract meaningful patterns, in particular in Music InformationRetrieval. Nevertheless, existing algorithms to compute NTD are mostly designedfor the Euclidean loss. This work proposes a multiplicative updates algorithmto compute NTD with the beta-divergence loss, often considered a better lossfor audio processing. We notably show how to implement efficiently themultiplicative rules using tensor algebra. Finally, we show on a musicstructure analysis task that unsupervised NTD fitted with beta-divergence lossoutperforms earlier results obtained with the Euclidean loss.

Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals