  In this paper, we present a comprehensive analysis of Acoustic Scene
Classification (ASC), the task of identifying the scene of an audio recording
from its acoustic signature. In particular, we firstly propose an
inception-based and low footprint ASC model, referred to as the ASC baseline.
The proposed ASC baseline is then compared with benchmark and high-complexity
network architectures of MobileNetV1, MobileNetV2, VGG16, VGG19, ResNet50V2,
ResNet152V2, DenseNet121, DenseNet201, and Xception. Next, we improve the ASC
baseline by proposing a novel deep neural network architecture which leverages
residual-inception architectures and multiple kernels. Given the novel
residual-inception (NRI) model, we further evaluate the trade off between the
model complexity and the model accuracy performance. Finally, we evaluate
whether sound events occurring in a sound scene recording can help to improve
ASC accuracy, then indicate how a sound scene context is well presented by
combining both sound scene and sound event information. We conduct extensive
experiments on various ASC datasets, including Crowded Scenes, IEEE AASP
Challenge 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 Task
1. The experimental results on several different ASC challenges highlight two
main achievements; the first is to propose robust, general, and low complexity
ASC systems which are suitable for real-life applications on a wide range of
edge devices and mobiles; the second is to propose an effective visualization
method for comprehensively presenting a sound scene context.
