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检索条件"机构=Laboratory of Advanced Design and Intelligent Computing"

共 237 条记录，以下是1-10 订阅

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Robust Keyword Spotting Based on Long-Term Feature Fusion and Band-Weighted Normalization 7

Robust Keyword Spotting Based on Long-Term Feature Fusion an...

引用

7th International Conference on Computer Information Science and Application Technology, CISAT 2024

作者： Qiu, Zepeng Zhao, Lasheng Wang, Ling Zhang, Tingting Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian China

ISBN: (纸本)9798350375107

Currently, convolution-based models for keyword spotting focus predominantly on research conducted in clean speech environments. However, their recognition accuracy is lower in low signal-to-noise ratio conditions. To enhance model robustness, we propose the long-term feature fusion module. By extracting and fusing long-term features in both the time and frequency domains, the model's perception of long-term features is strengthened, improving its recognition performance in noisy environments. At the same time, we propose a band-weighted normalization method, allowing the model to adjust the importance of different frequency bands during the normalization process, further enhancing the robustness of the model. Experimental results indicate that, on the Google Speech Command V2 dataset, the proposed model achieves higher recognition accuracy under various signal-to-noise ratios compared to the comparative models, with a lower parameter count. Moreover, the proposed model exhibits superior generalization to signal-to-noise ratios not covered during the training phase. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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Cross-Stage Mutual Distillation for Replay Attack Detection 9

Cross-Stage Mutual Distillation for Replay Attack Detection

引用

9th International Conference on intelligent computing and Signal Processing, ICSP 2024

作者： Cheng, Yinqing Zhao, Lasheng Wang, Ling Wang, Han Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian China

ISBN: (纸本)9798350376548

Automated speaker verification systems face a higher risk of replay attacks in practice. However, the existing studies face the problem of limited detection capabilities and insufficient use of shallow fine-grained information. To address these issues, we propose the cross-stage mutual distillation(CS-MD) framework, which involves two models learning from a deep network output of each other in different stages of training. This mutual learning approach enhances the ability of shallow networks to capture fine-grained speech information. Additionally, we use an attentional feature fusion module to integrate shallow information more effectively. The multi-scale attention mechanisms in this module can combine local and global speech features while preserving detailed information. Experimental results on the ASVspoof 2019 physical access dataset demonstrate that our proposed method outperforms state-of-the-art methods in terms of EER and min t-DCF metrics, validating the effectiveness of our CS-MD framework. © 2024 IEEE.

关键词： Speech enhancement

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A Color Image Encryption Algorithm Based on Complementary Map and Iterative Convolutional Code 24

A Color Image Encryption Algorithm Based on Complementary Ma...

引用

8th International Conference on Machine Learning and Soft computing, ICMLSC 2024

作者： Xie, Yanlu Zhou, Shihua Lv, Hui Wang, Bin Che, Chao Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9798400716546

Encryption is a valid means to safeguard the safety of images, and for color images, encryption should be performed considering the intrinsic correlation between R, G, and B components. In this paper, we propose an image encryption algorithm based on a complementary map and an iterative convolutional code. Firstly, the plain image is input into the convolutional encoders for iteration to generate the correctional secret key. Secondly, we design a new complementary map. From the test data, the new chaotic map has passed the NIST testing, exhibits a good chaotic characteristic, and has a wider range of chaotic parameters. Thirdly, global scrambling is performed on the color image to disrupt the distribution between R, G, and B. Then, a row-layer and a column-layer are randomly selected to form a set of elements to be encrypted. Finally, performing global diffusion on the image further increases the safety of our scheme. Experimental results show that our algorithm has a preferable encryption effect and elevated safety. © 2024 ACM.

关键词： Convolutional codes

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Acoustic Word Embedding Model with Transformer Encoder and Multivariate Joint Loss 11

Acoustic Word Embedding Model with Transformer Encoder and M...

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11th International Conference on intelligent computing and Wireless Optical Communications, ICWOC 2023

作者： Gao, Yunyun Zhang, Qiang Zhao, Lasheng Dalian University Ministry of Education Key Laboratory of Advanced Design and Intelligent Computing Dalian China

ISBN: (纸本)9798350321791

As a representation of speech information, acoustic word embedding can enable query-by-example keyword search with low-resource speech data. An acoustic word embedding model with Transformer encoder and multivariate joint loss is presented in this article. First of all, on the basis of BLSTM, the model can extract richer information by adding a Transformer encoder, which improves representation ability of the embedding vector. Then, the contrast loss is improved according to the multiple negative samples, and combined with the hinge loss with distance characteristics, the model generates a more compact vector representation and improves the identification of features. Finally, considering the unbalanced characteristics of samples, the anti-focal loss is introduced to form a multivariate joint loss together with the above two losses, so as to make the model performance better. The model has achieved certain results in word recognition tasks and has certain competitiveness compared with other acoustic word models. © 2023 IEEE.

关键词： Embeddings

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SCFNet: A Spatial-Channel Features Network Based on Heterocentric Sample Loss for Visible-Infrared Person Re-identification 16th

SCFNet: A Spatial-Channel Features Network Based on Heteroc...

引用

16th Asian Conference on Computer Vision, ACCV 2022

作者： Su, Peng Liu, Rui Dong, Jing Yi, Pengfei Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9783031262838

Cross-modality person re-identification between visible and infrared images has become a research hotspot in the image retrieval field due to its potential application scenarios. Existing research usually designs loss functions around samples or sample centers, mainly focusing on reducing cross-modality discrepancy and intra-modality variations. However, the sample-based loss function is susceptible to outliers, and the center-based loss function is not compact enough between features. To address the above issues, we propose a novel loss function called Heterocentric Sample Loss. It optimizes both the sample features and the center of the sample features in the batch. In addition, we also propose a network structure combining spatial and channel features and a random channel enhancement method, which improves feature discrimination and robustness to color changes. Finally, we conduct extensive experiments on the SYSU-MM01 and RegDB datasets to demonstrate the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Infrared imaging

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MFN: Explainable DNA triple helixes Stabilized design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wen, Xiaoru Sun, Lijun Xie, Lei Zheng, Yanfen Cao, Ben Wang, Bin Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350386226

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy. © 2024 IEEE.

关键词： Crosstalk

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Speech Emotion Recognition using Channel Attention Mechanism 4

Speech Emotion Recognition using Channel Attention Mechanism

引用

4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Zhu, Ruifeng Sun, Caixia Wei, Xiaopeng Zhao, Lasheng Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9798350347548

In order to improve the accuracy of speech emotion recognition, this paper proposes a speech emotion recognition method based on the channel attention mechanism. Firstly, Mel Frequency Ceptral Coefficient(MFCC), speech spectrograms and spectral envelopes are selected as the initial input features;then, multiple depth network models are used to extract feature maps from different angles in parallel;then, weights are assigned and fused to the feature maps output from each sub-depth network model by the channel attention mechanism;finally, the fused feature maps are used to predict emotion categories. The experimental results on CASIA, Emo-DB, and SAVEE emotion datasets show that the method achieves 88.3%, 85.1%, and 64.5% recognition accuracy, respectively, with better recognition performance compared to recent comparative literature models. © 2023 IEEE.

关键词： Emotion Recognition

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A Text Structure-based Extractive And Abstractive Summarization Method 7

A Text Structure-based Extractive And Abstractive Summarizat...

引用

7th International Conference on intelligent computing and Signal Processing, ICSP 2022

作者： Yan, Jing Zhou, Shihua Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China

ISBN: (纸本)9781665478571

Extraction summarization and abstraction summarization have advantages and disadvantages, so how to better combine these two ways has become a difficult problem. To address this challenge, this paper proposed a new fusion method. The major novelty lies in the design of the new method. Wherein our approach, first, use the text structure information and the idea of the K-means algorithm to divide the text into regions, and then the main part and the non-main parts are determined according to the distribution of the subject words. Next, apply information extraction on the main part and text generation on the non-main parts. Finally, the two parts of the summarization are merged according to the sequence of the text. Experimental results show that the quality of the summarization is better than that of extraction summarization and abstraction summarization. In addition, to make the method more targeted in Chinese text processing, the Cw2vec model based on Chinese stroke information is used in the encoding process, and the experiment proves that the quality of summarization can be further improved. © 2022 IEEE.

关键词： Text processing

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Cross-Stage Mutual Distillation for Replay Attack Detection

Cross-Stage Mutual Distillation for Replay Attack Detection

引用

6th International Conference on intelligent computing and Signal Processing (ICSP)

作者： Yinqing Cheng Lasheng Zhao Ling Wang Han Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

关键词： Training Voice activity detection Measurement Knowledge engineering Attention mechanisms Speech enhancement Signal processing Feature extraction Faces

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Semi-Supervised Semantic Segmentation Based on Class Balanced Framework

Semi-Supervised Semantic Segmentation Based on Class Balance...

引用

IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Jiacheng Yang Jing Dong Pengfei Yi Rui Liu Ling Wang The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Semi-supervised semantic segmentation is a task that utilizes a small amount of labeled data and a large amount of unlabeled data to achieve pixel-level annotation. Due to the common long tail distribution phenomenon in semi-supervised semantic segmentation datasets and the fixed threshold strategy commonly used in filtering pseudo labels, the model tends to favor the head classes when generating pseudo labels, resulting in poor segmentation performance on tail data. This article proposes a semi-supervised semantic segmentation method based on a class- balanced framework, which improves the model's recognition ability for minority classes and the quality of pseudo labels through tail classes oversampling and dynamic threshold adjustment. This method first constructs a Class Prediction Sampling-based Self-balancing Module (CPS 2 M). It identifies tail classes through Intersection-over-Union (IoU) analysis and constrains the random oversampling process based on the tail classes data distribution degree. Subsequently, the sampled labeled data is mixed with unlabeled samples to transfer prior knowledge, increase training frequency for tail classes, and solve the experience mismatch problem. Finally, we design a Dynamic Threshold Updating module (DTU) based on a non-linear function. The confidence threshold can dynamically change according to the model's learning ability to avoid discarding correct tail classes with low confidence, which alleviates the class imbalance problem of generating pseudo-labels. Based on the DeepLabV3+ network framework and ResNet-101 pre-training model, this method achieves 82.1 mIoU under 1464 labeled partition protocols on the Classic Pascal VOC 2012 dataset, demonstrating the most advanced performance compared to existing methods.

关键词： Training Protocols Filtering Annotations Semantic segmentation Semantics Tail

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