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 event localization and detection (SELD) involves identifying thedirection-of-arrival (DOA) and the event class. The SELD methods with aclass-wise output format make the model predict activities of all sound eventclasses and corresponding locations. The class-wise methods can outputactivity-coupled Cartesian DOA (ACCDOA) vectors, which enable us to solve aSELD task with a single target using a single network. However, there is stilla challenge in detecting the same event class from multiple locations. Toovercome this problem while maintaining the advantages of the class-wiseformat, we extended ACCDOA to a multi one and proposed auxiliary duplicatingpermutation invariant training (ADPIT). The multi- ACCDOA format (a class- andtrack-wise output format) enables the model to solve the cases with overlapsfrom the same class. The class-wise ADPIT scheme enables each track of themulti-ACCDOA format to learn with the same target as the single-ACCDOA format.In evaluations with the DCASE 2021 Task 3 dataset, the model trained with themulti-ACCDOA format and with the class-wise ADPIT detects overlapping eventsfrom the same class while maintaining its performance in the other cases. Also,the proposed method performed comparably to state-of-the-art SELD methods withfewer parameters.

Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training