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Found 5 papers, 3 papers with code
A Two-Stage Approach to Device-Robust Acoustic Scene Classification
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed.
Ranked #1 on
Acoustic Scene Classification
on TAU Urban Acoustic Scenes 2019
(using extra training data)
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances
In contrast to building scene models with whole utterances, the ASM-removed sub-utterances, i. e., acoustic utterances without stop acoustic segments, are then used as inputs to the AlexNet-L back-end for final classification.
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
On Task 1b development data set, we achieve an accuracy of 96. 7\% with a model size smaller than 500KB.
DocUNet: Document Image Unwarping via a Stacked U-Net
The network is trained on this dataset with various data augmentations to improve its generalization ability.
Ranked #3 on
Local Distortion
on DocUNet
(using extra training data)
Coherent Parametric Contours for Interactive Video Object Segmentation
Interactive video segmentation systems aim at producing sub-pixel-level object boundaries for visual effect applications.
