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15th IEEE International Conference on Advanced Infocomm technology, ICAIT 2023

作者： Qi, Meibin Ren, Weijie Xiang, Houhong Hefei University of Technology Institude of Signal and Information Processing Hefei230601 China Hefei University of Technology Radar Technology and Innovative Application Laboratory Hefei230601 China

ISBN: (纸本)9798350314120

Aiming at the common mixed sources including near-field(NF) and far-field(FF), this paper proposes a mixed sources direction-of-arrival(DOA) estimation method based on Deep Learning(DL). By using the real part and imaginary part of the mixed sources as the input of the neural network, after full separation, the expected FF signals and NF signals can be obtained at the output of the network. Then, the traditional estimation methods for a single source can be used to estimate DOA. The proposed method optimizes some defects existing in the estimation of mixed sources containing near and far fields in the past, such as the inability to separate the mixed sources effectively, and a large loss of array aperture. Simulation results show that the proposed method is effective. Under different SNR or channel error conditions, the proposed method has better DOA estimation accuracy and generalization ability than most super-resolution DOA estimation algorithms. © 2023 IEEE.

关键词： radar antennas

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Improving Speech Perceptual Quality and Intelligibility Through Sub-band Temporal Envelope Characteristics 18th

Improving Speech Perceptual Quality and Intelligibility Thr...

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Wu, Ruilin Huang, Zhihua Song, Jingyi Liang, Xiaoming School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing in Xinjiang Urumqi China

ISBN: (纸本)9789819706006

In the speech enhancement (SE) model, using auxiliary loss based on acoustic parameters can improve enhancement effects. However, currently used acoustic parameters focus on frequency domain information, neglecting the importance of time domain information. This study explores the effects of training the SE model utilizing temporal envelope parameters as auxiliary losses on perceptual quality and intelligibility, primarily focusing on accurately extracting temporal envelope parameters and optimizing losses. Initially, the effectiveness and robustness of the temporal envelope parameters in optimizing the SE model were verified. Subsequently, a temporal envelope loss based on sub-band weighted (ENVLoss) is proposed based on the perceptual characteristics of human hearing. Then, a method for constructing a joint loss function is proposed, integrating temporal and spectral acoustic features to promote the enhancement effect. Experimental results show that temporal envelope characteristics improve both the time domain and the time-frequency domain SE models. Compared to other acoustic parameter losses, the SE model using a sub-band temporal envelope auxiliary loss shows improvement in the PESQ, STOI, and MOS estimation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Speech intelligibility

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MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screening Attention Network for Urban Carbon Emission Prediction

MSFSAN: A Novel Multi-Scale Spatio-Temporal Feature Screenin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Ben Qin, Xizhong Qin, Jiwei Zhang, Xiaoyu Ma, Haodong College of Computer Science and Technology Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing Ürümqi830046 China

ISBN: (纸本)9781665410205

In order to cope with the increasingly severe global energy conservation and emission reduction problems, research on urban carbon emission prediction is of great significance. The existing methods mainly use time series analysis to predict urban carbon emission, but there is a strong spatial correlation between the carbon emission data of several cities. Therefore, this paper designs a multi-scale spatial-temporal feature screening attention network to predict target cities' future carbon emission data. Firstly, this paper combines the daily carbon emission data of the near-neighbouring cities and the daily homologous emission data of the target city to analyze the urban carbon emission data from a spatio-temporal perspective. Then, this paper designs a multi-scale spatial interactive convolution module and a multi-scale temporal convolution module to extract multi-scale spatio-temporal features effectively. In addition, the feature screening module is designed to reduce the adverse effects of redundant features. Finally, the multi-scale features are used to predict the future carbon emissions of the target city through a predictor. The experimental results show that our prediction model is superior to the existing methods in six datasets. © 2024 IEEE.

关键词： Low emission

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A Lightweight Music Source Separation Model with Graph Convolution Network 1

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Zhu, Mengying Wang, Liusong Hu, Ying School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing in Xinjiang Urumqi China

ISBN: (数字)9789819706013

ISBN: (纸本)9789819706006

With the rapid advancement of deep neural networks, there has been a significant improvement in the performance of music source separation methods. However, most of them primarily focus on improving their separation performance, while ignoring the issue of model size in the real-world environments. For the application in the real-world environments, in this paper, we propose a lightweight network combined with the Graph convolutional network Attention (GCN_A) module for Music Source Separation (G-MSS), which includes an Encoder and four Decoders, each of them outputs a target music source. The G-MSS network adopts both time-domain and frequency-domain L1 losses. The ablation study verifies the effectiveness of our designed GCN Attention (GCN_A) module and multiple Decoders, and also make a visualization analysis of the main components in the G-MSS network. Comparing with the other 13 methods on the MUSDB18 dataset, our proposed G-MSS achieves comparable separation performance while maintaining the lower amount of parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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HF signal Protocol Unique Sequence Classification based on Deep Learning 16

HF Signal Protocol Unique Sequence Classification based on D...

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16th International Conference on signal processing Systems, ICSPS 2024

作者： Li, Weihao Wang, Keren You, Ling Huang, Zhitao Xiong, Gang College of Electronic Science and Technology National University of Defense Technology Changsha410073 China National Key Laboratory on Blind Signal Processing Chengdu610041 China

ISBN: (纸本)9781510689251

This paper studies the classification of high frequency (HF) signal unique sequence like the preamble, which helps to identify the HF communication protocol. Targeting at a processing flow of sliding window-based unique sequence classification in the narrowband data, and considering the disadvantages of the traditional unique sequence template correlation method, such as relying on the unique sequence knowledge and its strong correlation, slow calculation speed and the calculation times increase with the number of classes, this paper uses deep learning technology to classify the unique sequence classes. The main improvements are that, based on the two classical convolutional neural networks, the relationship between the model receptive field and signal symbol rate is analyzed and adjusted accordingly;and in order to make the local symbol features extracted by convolutional network context-related, a multi-head self-attention layer is introduced behind it. In the experiments, 7 types of real-world signals and background signals are used. The results show that the improved neural networks obtain classification F1 performances similar to that of the traditional high-precision template correlation method, and their processing speed is dozens of times faster than the latter. © 2025 SPIE.

关键词： Convolutional neural networks

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Multi-speaker Emotional Speech Synthesis Based on Self-Supervised Speech Representations 5

Multi-speaker Emotional Speech Synthesis Based on Self-Super...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Yin, Mingming Huang, Zhihua Lu, Kexin Zhou, Haiyang School of Computer Science and Technology Xinjiang University Urumqi China School of Computer Science and Technology Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350355925

In recent years, emotional speech synthesis has shown considerable progress. However, some existing emotional speech synthesis methods only model emotion from a single scale, resulting in only global or average emotional expression, unable to synthesize significant emotional speech. In this paper, we propose a multi-scale multi-speaker emotional speech synthesis method based on self-supervised speech model HuBERT. Firstly, the emotion information is extracted from the feature extractor HuBERT. Secondly, the extracted emotion information is modeled by coarse-grained emotion modeling and fine-grained emotion modeling respectively to obtain utterance-level emotion embedding and phoneme-level emotion embedding. Through subjective and objective evaluation, we verify that the method is significantly better than the comparison model in terms of speech quality and emotional expression. © 2024 IEEE.

关键词： emotional speech synthesis self-supervised speech representation speech synthesis

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Shared Vision Transformer Helps Scene Text Retrieval 4th

Shared Vision Transformer Helps Scene Text Retrieval

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Luo, Hailong Ibrayim, Mayire Hamdulla, Askar Deng, Qilin School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China

ISBN: (纸本)9789819787043

The scene text retrieval system can search all the images containing the query text in the gallery based on the input query text and locate the position of the query text at the same time. The current state-of-the-art approach is to match query images and text instances based on visual information, which somewhat mitigates the heterogeneous differences between the two modalities and can support the zero-shot training approach. But there is no consideration of two branches of features interacting on information as a way to enhance the features. In this paper, we improve this method. First, we design a structure that enhances features using a shared-visual embedding transformer (S-VET). Text instance features and query input features are brought closer visually by a visual transformer with shared parameters and then spliced and fused with the original features to enrich the visual information of the two branch features. We then design a Multi-Scale Residual Channel Attention (MRCA) module to obtain features at different scales by using convolution kernels at multiple scales and then use channel attention to mine beneficial information from features at different scales. Facilitated by the above scheme, our method achieved a more advanced performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Improving ECAPA-TDNN Performance with Coordinate Attention

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Journal of Shanghai Jiaotong University (science) 2024年 1-7页

作者： Liu, Shuanghong Song, Zhida He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The current mainstream networks, such as squeeze and excitation residual neural network (SE-ResNet) and emphasized channel attention, propagation and aggregation based time delay neural network (ECAPA-TDNN), enhance the capability of speaker embedding extractors to extract more discriminative speaker embeddings by incorporating squeeze and excitation (SE) attention within the convolutional blocks. However, the SE attention focuses solely on encoding inter-channel information, overlooking the importance of spatial positional information and time-frequency information, which are crucial for the model’s performance. In this paper, we first experimentally compare the effectiveness of several mainstream attention mechanisms in the computer vision domain for the ECAPA-TDNN model. Next, we focus on the substantial improvements that coordinate attention (CA) brings to the ECAPA-TDNN model. The introduction of CA can help the model embed time-frequency information into the channel representation. Even without using AS-Norm, our proposed model achieves relative reductions of about 5.3% equal error rate (EER) and 5.5% minimum detection cost function (minDCF) on both the Voxceleb-O and Voxceleb-H test sets compared to the ECAPA-TDNN baseline model. In addition, the EER is relatively reduced by 9.46% on the CN-Celeb1 test set. This result strongly demonstrates that the CA module can effectively improve the generalization ability of the ECAPA-TDNN model. © Shanghai Jiao Tong University 2024.

关键词： Neural network models

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MTKD-Det: An Efficient SAR Target Detector Based on Multi-Teacher Knowledge Distillation 2

MTKD-Det: An Efficient SAR Target Detector Based on Multi-Te...

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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Zhang, Guang Wang, Zhaocheng Wang, Ruonan Song, Hualiang Kang, Hailong Hebei University of Technology School of Electronics and Information Engineering Tianjin China Xidian University Hangzhou Institute of Technology Hangzhou China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798331515669

As a critical branch of synthetic aperture radar (SAR) interpretation, SAR target detection has many applications in the military and civil fields. Although traditional deep learning methods achieve some satisfactory performance in target detection tasks, they inherently require significant computational resources and interpretation time. Hence, traditional deep learning methods frequently struggle to maintain a balance between high efficiency and precision. It is undoubtedly a huge challenge for target detection tasks. To address this problem, an efficient SAR target detector based on multi-teacher knowledge distillation (MTKD-Det) is proposed in this paper, which can effectively enhance target detection accuracy while reducing inference time. Specifically, a multiple-teacher knowledge distillation strategy is used in the proposed method, which integrates the experiences and methodologies of multiple teachers to collaboratively guide the training and learning of the student network, ultimately enhancing the robustness of the student network. In this work, a novel teacher network is developed to enhance the detection performance of the proposed method specifically for small targets. Furthermore, an attention mechanism is incorporated into the student network to enable it to focus on more critical information, thereby improving overall detection efficacy. Experimental results obtained from a measured SAR target detection dataset convincingly validate the benefits of the proposed approach. © 2024 IEEE.

关键词： Synthetic aperture radar

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A Refined Tracking Filtering Algorithm Based on IMM 12

A Refined Tracking Filtering Algorithm Based on IMM

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12th IEEE International Conference on Control, Automation and Information sciences, ICCAIS 2023

作者： Jiao, Hao Liu, Yudong Yan, Junkun Liu, Hongwei Xidian University National Key Laboratory of Radar Signal Processing Xi'an China China Academy of Launch Vehicle Technology Department of Research and Development China

ISBN: (纸本)9798350328783

Maneuvering targets, distinguished by their pronounced dynamic characteristics, present significant challenges to radar tracking. To address these problems, a refined tracking method based on IMM (IMM-RTF) is proposed in this paper. The key idea of the method is to adaptively correct the recursive residual covariance by using the observation residual based on the consistency of residual and residual covariance to reduce the influence of model mismatch. In addition, to realize refined filtering, this paper analyzes the influence of model mismatch on the filter, and models the consistency problem of residual and residual covariance as a convex optimization problem. By analyzing the unique structure of the problem, the analytical solution of the problem under two assumptions is derived. Meanwhile, the adaptive IMM framework is adopted, thus effectively improving the tracking performance of maneuvering targets and non-maneuvering targets under steady-state conditions. Simulation results demonstrate that compared with the IMM, the IMM-RTF not only accentuates the tracking precision for maneuvering targets in steady states but also significantly fortifies target tracking robustness amidst model mismatch. © 2023 IEEE.

关键词： Target tracking

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