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

Sound source localization is crucial in acoustic sensing andmonitoring-related applications. In this paper, we do a comprehensive analysisof improvement in sound source localization by combining the direction ofarrivals (DOAs) with their derivatives which quantify the changes in thepositions of sources over time. This study uses the SALSA-Lite feature with aconvolutional recurrent neural network (CRNN) model for predicting DOAs andtheir first-order derivatives. An update rule is introduced to combine thepredicted DOAs with the estimated derivatives to obtain the final DOAs. Theexperimental validation is done using TAU-NIGENS Spatial Sound Events (TNSSE)2021 dataset. We compare the performance of the networks predicting DOAs withderivative vs. the one predicting only the DOAs at low SNR levels. The resultsshow that combining the derivatives with the DOAs improves the localizationaccuracy of moving sources.

Improving trajectory localization accuracy via direction-of-arrival derivative estimation