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

Considering that acoustic scenes and sound events are closely related to eachother, in some previous papers, a joint analysis of acoustic scenes and soundevents utilizing multitask learning (MTL)-based neural networks was proposed.In conventional methods, a strongly supervised scheme is applied to sound eventdetection in MTL models, which requires strong labels of sound events in modeltraining; however, annotating strong event labels is quite time-consuming. Inthis paper, we thus propose a method for the joint analysis of acoustic scenesand sound events based on the MTL framework with weak labels of sound events.In particular, in the proposed method, we introduce the multiple-instancelearning scheme for weakly supervised training of sound event detection andevaluate four pooling functions, namely, max pooling, average pooling,exponential softmax pooling, and attention pooling. Experimental resultsobtained using parts of the TUT Acoustic Scenes 2016/2017 and TUT Sound Events2016/2017 datasets show that the proposed MTL-based method with weak labelsoutperforms the conventional single-task-based scene classification and eventdetection models with weak labels in terms of both the scene classification andevent detection performances.

Joint Analysis of Acoustic Scenes and Sound Events with Weakly labeled Data