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/

Zendy Plus

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

Zendy Tools

Zendy Open

In this paper, we present a comprehensive analysis of Acoustic SceneClassification (ASC), the task of identifying the scene of an audio recordingfrom its acoustic signature. In particular, we firstly propose aninception-based and low footprint ASC model, referred to as the ASC baseline.The proposed ASC baseline is then compared with benchmark and high-complexitynetwork architectures of MobileNetV1, MobileNetV2, VGG16, VGG19, ResNet50V2,ResNet152V2, DenseNet121, DenseNet201, and Xception. Next, we improve the ASCbaseline by proposing a novel deep neural network architecture which leveragesresidual-inception architectures and multiple kernels. Given the novelresidual-inception (NRI) model, we further evaluate the trade off between themodel complexity and the model accuracy performance. Finally, we evaluatewhether sound events occurring in a sound scene recording can help to improveASC accuracy, then indicate how a sound scene context is well presented bycombining both sound scene and sound event information. We conduct extensiveexperiments on various ASC datasets, including Crowded Scenes, IEEE AASPChallenge on Detection and Classification of Acoustic Scenes and Events (DCASE)2018 Task 1A and 1B, 2019 Task 1A and 1B, 2020 Task 1A, 2021 Task 1A, 2022 Task1. The experimental results on several different ASC challenges highlight twomain achievements; the first is to propose robust, general, and low complexityASC systems which are suitable for real-life applications on a wide range ofedge devices and mobiles; the second is to propose an effective visualizationmethod for comprehensively presenting a sound scene context.

Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context