Robust Scene Classification with Cross-Level LLC Coding on CNN Features

2015 ◽  
pp. 376-390 ◽  
Author(s):  
Zequn Jie ◽  
Shuicheng Yan
Keyword(s):  
Scene Classification

Temporal Transformer Networks for Acoustic Scene Classification

2018 ◽  
Author(s):  
Teng Zhang ◽  
Kailai Zhang ◽  
Ji Wu
Keyword(s):  
Scene Classification

Multiobjective ResNet pruning by means of EMOAs for remote sensing scene classification

2020 ◽  
Vol 381 ◽  
pp. 298-305 ◽  
Author(s):  
Xuning Liu ◽  
Yong Zhou ◽  
Jiaqi Zhao ◽  
Rui Yao ◽  
Bing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Scene Classification

scholarly journals CNN-Based Acoustic Scene Classification System

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 371
Author(s):  
Yerin Lee ◽  
Soyoung Lim ◽  
Il-Youp Kwak
Keyword(s):  
Low Complexity ◽  
Classification Model ◽  
Weight Class ◽  
Scene Classification ◽  
Audio File ◽  
General Classification ◽  
Team Task ◽  
Real World Applications ◽  
Activation Mapping

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcome the aforementioned problems. First, a more general classification model was proposed by combining the harmonic-percussive source separation (HPSS) and deltas-deltadeltas features with four different models. Second, using the same feature, depthwise separable convolution was applied to the Convolutional layer to develop a low-complexity model. Moreover, using gradient-weight class activation mapping (Grad-CAM), we investigated what part of the feature our model sees and identifies. Our proposed system ranked 9th and 7th in the competition for these two subtasks, respectively.

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