CNN-based Speech Emotion Recognition using Transfer Learning

Heewon, Choee; Park, Seung Min; KweeBoSIM,

doi:10.5391/jkiis.2019.29.5.339

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2021

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“…When applying a convolutional neural network (CNN) [8][9][10], three problems occur: a reduction in the resolution, a loss of information, objects, and space invariance. These problems are the first, occurs in the pooling process, the second, is generated by the existence of an object between the layers and the layer or between the extracted feature map.…”

Section: Introductionmentioning

confidence: 99%

MPFANet: Semantic Segmentation Using Multiple Path Feature Aggregation

Jeon¹,

Rhee²

2021

IJFIS

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Image segmentation is the process of simplifying the analysis of the meaning or the front to say the process of dividing the image into a set of multiple pixels. The multiple path feature aggregation (MPFA) method proposed in this paper aims to extract various information of an object, and uses conventional pyramid pooling or the extraction of various sized features. This information can be combined with different regional features to obtain the overall feature information. We split four paths to extract numerous local features, and the results showed that the mean intersection over union (mIOU) is 81.6% for the validation data from the PASCAL VOC 2012 dataset, and a better performance than the existing DeepLab model was demonstrated.

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Section: Introductionmentioning

confidence: 99%