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Kwak, Hyok
- Neighborhood Loss for Age Estimation from Face Image Using Convolutional Neural Networks
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Authors
Affiliations
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, KP
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, KP
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2770-2774Abstract
Convolutional Neural Network (CNN) is widely used in estimating age from face image. In many CNN applications such as image classification, face recognition and other computer vision scopes, the cross-entropy loss is used as a supervision signal to train CNN model. However, the cross-entropy loss only enhances the separability of classes and does not consider their correlation in age estimation task. In this paper we propose a novel loss function called neighborhood loss which regards the correlation between classes in age estimation by modifying standard cross entropy loss. To evaluate the effectiveness of the proposed neighborhood loss, we present CNN architecture based on the residual units. Through some experiments, we show that neighborhood loss provides superior performance compared to prior works in age estimation.Keywords
Age Estimation, Neighborhood Loss, Convolutional Neural Network.
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- Multichannel Speech Enhancement of Target Speaker Based on Wakeup Word Mask Estimation with Deep Neural Network
Abstract Views :92 |
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Authors
Affiliations
1 Institute of Information Technology, Hightech Research & Development Center Kim Il Sung University, Pyongyang, KP
1 Institute of Information Technology, Hightech Research & Development Center Kim Il Sung University, Pyongyang, KP
Source
International Journal of Advanced Networking and Applications, Vol 15, No 1 (2023), Pagination: 5754-5759Abstract
In this paper, we address a multichannel speech enhancement method based on wakeup word mask estimation using Deep Neural Network (DNN). It is thought that the wakeup word is an important clue for target speaker. We use a DNN to estimate the wakeup word mask and noise mask and apply them to separate the mixed wakeup word signal into target speaker’s speech and background noise. Convolutional Recurrent Neural Network (CRNN) is used to exploit both short and long term time-frequency dependencies of sequences such as speech signals. Generalized Eigen Vector (GEV) beamforming estimates the spatial filter by using the masks to enhance the following speech command of target speaker and reduce undesirable noise. Experiment results show that the proposal provides more robust to noise, so that improves the Signal-to-Noise Ratio (SNR) and speech recognition accuracy.Keywords
Multichannel Speech Enhancement, Wakeup Word, Mask Estimation, Beamforming, Deep Neural Network (DNN).Full Text
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