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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The analysis of underwater acoustic signals, especially ship-radiated noise received by passive sonar, is of great importance in theelds of defense, military, and scientic research. In this paper, we investigate multiple kernel learning graph embedding using auditory model features in the application of ship-radiated noise feature extraction. We use an auditory model to get auditory model features for each signal sample. In order to have more effective features, iterative multiple kernel learning methods are adopted to conduct dimensionality reduction. Validated by experiments, the proposed method outperforms ordinary kernel-based graph embedding methods. The experiments show that the multiple kernel learning method can automatically choose relatively appropriate kernel combinations in dimensionality reduction for ship-radiated noise using auditory model features. In addition, some worthwhile conclusions can be drawn from our experiments and analysis.

Ship-radiated noise feature extraction using multiple kernel graph embedding and auditory model