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Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition

作者： Xiangqian Zhao Xuebin Xu Reyihanguli Kasenmu Xinjiang University China Xinjiang Key Laboratory of Signal Detection and Processing(Xinjiang University) Xinjiang University China Xinjiang Teachers college China

ISBN: (纸本)9798400707988

In the process of printed document image retrieval, the traditional algorithm, SURF algorithm combined with violent matching, has the problems of low retrieval accuracy and low retrieval efficiency. This paper proposes a FAST + PCA-SURF combined with KNN algorithm based on FLANN for multilingual document image retrieval. Based on FAST, the feature points are detected, and the feature descriptors after dimensionality reduction are extracted by ***, the KNN algorithm based on FLANN is used for feature matching, and finally the appropriate matching results are output. The experimental results show that the proposed algorithm is improved in terms of time complexity and retrieval accuracy compared with the traditional algorithm. The average time complexity and retrieval accuracy of the traditional algorithm are 0.1783 s and 71.8%, respectively, while the proposed algorithm is 0.0464 s and 77.8%, indicating that the proposed algorithm achieves better experimental results in multilingual document image retrieval.

关键词： FAST FLANN matching PCA-SURF Printed multilingual document images keyword search.

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Multi-agricultural Machinery Collaborative Task Assignment Based on Improved Genetic Hybrid Optimization Algorithm

arXiv

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arXiv 2023年

作者： Du, Haohao Lai, Huicheng Gao, Guxue Wen, Xiaopeng Li, Yifei Wang, Haidong Zhang, Guo College of Information Science and Engineering Xinjiang University Urumqi 830017 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi 830017 China

Agricultural machinery collaboration technology is crucial in improving the efficiency of agricultural machinery utilisation and enhancing the benefits of large-scale production of machinery fleets. To address the challenges of delayed scheduling information, heavy reliance on manual labour, and low operational efficiency in traditional large-scale agricultural machinery operations, this study proposes a method for multi-agricultural machinery collaborative task assignment based on an improved genetic hybrid optimisation algorithm. The proposed method establishes a multi-agricultural machinery task allocation model by combining the path pre-planning of a simulated annealing algorithm and the static task allocation of a genetic algorithm. By sequentially fusing these two algorithms, their respective shortcomings can be overcome, and their advantages in global and local search can be utilised. Consequently, the search capability of the population is enhanced, leading to the discovery of more optimal solutions. Then, an adaptive crossover operator is constructed according to the task assignment model, considering the capacity, path cost, and time of agricultural machinery;two-segment coding and multi-population adaptive mutation are used to assign tasks to improve the diversity of the population and enhance the exploration ability of the population;and to improve the global optimisation ability of the hybrid algorithm, a 2-Opt local optimisation operator and an Circle modification algorithm are introduced. Finally, simulation experiments were conducted in MATLAB to evaluate the performance of the multi-agricultural machinery collaborative task assignment based on the improved genetic hybrid algorithm. The algorithm's capabilities were assessed through comparative analysis in the simulation trials. The results demonstrate that the developed hybrid algorithm can effectively reduce path costs, and the efficiency of the assignment outcomes surpasses that of the classical g

关键词： Genetic algorithms

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LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Verification 14

LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Ver...

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14th International Symposium on Chinese Spoken Language processing, ISCSLP 2024

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

ISBN: (纸本)9798331516826

Due to its superior performance and fewer parameters, CAM++ has become the state-of-the-art model for speaker verification tasks. This model uses 2D convolutional blocks to extract front-end features, which are then fed into a densely connected time-delay neural network backbone to extract deep features. However, the simple stacking of 2D convolutions may lead to the generation of a significant amount of redundant features, which is detrimental to efficient feature extraction. Furthermore, although CAM++ already has a relatively small number of parameters, there is still room for further optimization. To address these issues, this paper first employs depthwise separable convolutions to replace the dilated convolutions in the backend network of CAM++, making the model more lightweight. Next, we introduce spatial and channel reconstruction convolution (SCConv) in the ResBlock module of CAM++ to reduce redundant features and optimize the feature extraction process. Finally, after SCConv, we apply squeeze and excitation attention mechanism to model the interdependencies between channels and recalibrate each channel, further enhancing the model’s representational capacity. We name the resulting model LE-CAM++. Our proposed model achieves an EER of 0.686 and a minDCF of 0.084 on the VoxCeleb1-O dataset. Compared to the baseline model CAM++, the EER is reduced by 11%, and the minDCF is reduced by 28%. Additionally, the model parameters are reduced by 8%. ©2024 IEEE.

关键词： Deep neural networks

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Design and Application of Pocket Experiment System Based on STM32F4 2020

Design and Application of Pocket Experiment System Based on ...

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2020 The 8th International Conference on Information Technology: IoT and Smart City

作者： YANYUN ZHOU FEI SHI JUAN CHEN School of information science and engineering Xinjiang University and Key Laboratory of Signal Detection and Processing China School of information science and engineering Xinjiang University China

ISBN: (纸本)9781450388559

Aiming at the disadvantages of embedded system in practical teaching, a pocket experiment system based on STM32F4 is developed. The system includes input unit, output unit, logic control unit, power supply unit, peripheral unit and data transmission unit. It has the characteristics of rich resources, low cost, and easy to carry, which meets the needs of students' mobility experiments and extra-curricular innovation practice training. Practical application shows that the system not only stimulates students' enthusiasm for learning, but also trains their creative ability and practical application ability.

关键词： teaching reform pocket experiment system innovation practice

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Introducing Multilingual Phonetic Information to Speaker Embedding for Speaker Verification

Introducing Multilingual Phonetic Information to Speaker Emb...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhida Song Liang He Penghao Wang Ying Hu Hao Huang School of Computer Science and Technology Xinjiang University China Xinjiang Key Laboratory of Signal Detection and Processing China Department of Electronic Engineering Tsinghua University China

Incorporating frame-level phonetic information during the extraction of speaker embeddings has been shown to enhance the performance of speaker verification systems. However, previous studies have primarily relied on phonetic information obtained from pre-trained models of monolingual automatic speech recognition (ASR). Considering that speaker verification datasets typically consist of multiple languages, there are instances where speakers are proficient in multiple languages, resulting in discrepancies between the languages used in the enrolled and test utterances. To address these challenges, we employ a pre-trained multilingual ASR Conformer encoder to initialize the MFA-Conformer network for speaker verification. Experimental results on the VoxCeleb dataset demonstrate a significant improvement in the performance of the system that incorporates multilingual phonetic information across different evaluation sets, including VoxCeleb1-O, E, and H, as well as the VoxSRC21 validation set, which focuses on multilingual verification. The source code is released at https://***/zds-potato/multilingual-phonetic-sv.

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

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

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

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

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

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.

关键词： Speech recognition Machine learning Feature extraction Speech synthesis Data mining

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A Hybrid Recommendation Algorithm with Co-embedded Item Attributes and Ratings

A Hybrid Recommendation Algorithm with Co-embedded Item Attr...

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International Conference on Applied Machine Learning (ICAML)

作者： Gaowei Xin Jiwei Qin Jiong Zheng School of Information Science and Engineering Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi China

In today's recommendation algorithm field, attribute feature information of users or items is usually introduced as auxiliary information to alleviate the sparsity problem, which can achieve good recommendation results to some extent. To make better use of the auxiliary information for data compression and dimensionality reduction, autoencoder models are usually employed. However, existing autoencoder-based models require the same input and output dimensions, resulting in data information loss and poor autoencoder scalability, which ultimately leads to low recommendation line performance. To cope with this problem, we propose a hybrid collaborative recommendation algorithm with co-embedded item attributes and ratings (HCR-item). By one-hot encoding the discrete item attribute features and then fusing the rating features with the item attribute features as the input of the autoencoder, the autoencoder completes the data compression and reconstructs the rating interaction prediction matrix. The algorithm proposed in this paper integrates item attribute features and rating features together, flexibly uses different feature information, extends the feature space, and solves the problem of low recommendation performance caused by sparsity. The algorithm has been extensively experimented on several real datasets and compared with other algorithms, and the results show that HCR-item has better recommendation performance.

关键词： Dimensionality reduction Scalability Data compression Collaboration Predictive models Prediction algorithms Data models

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A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition

arXiv

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arXiv 2022年

作者： Hu, Ying Tang, Yuwu Huang, Hao He, Liang Key Laboratory of Signal Detection and Processing in Xinjiang China School of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University China

Speech emotion recognition (SER) is an essential part of human-computer interaction. In this paper, we propose an SER network based on a Graph Isomorphism Network with Weighted Multiple Aggregators (WMA-GIN), which can effectively handle the problem of information confusion when neighbour nodes' features are aggregated together in GIN structure. Moreover, a Full-Adjacent (FA) layer is adopted for alleviating the over-squashing problem, which is existed in all Graph Neural Network (GNN) structures, including GIN. Furthermore, a multi-phase attention mechanism and multi-loss training strategy are employed to avoid missing the useful emotional information in the stacked WMA-GIN layers. We evaluated the performance of our proposed WMA-GIN on the popular IEMOCAP dataset. The experimental results show that WMA-GIN outperforms other GNN-based methods and is comparable to some advanced non-graph-based methods by achieving 72.48% of weighted accuracy (WA) and 67.72% of unweighted accuracy (UA). Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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BD-YOLO: Optimising Insect Imbalance Data detection Models

BD-YOLO: Optimising Insect Imbalance Data Detection Models

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jian–Yi Wang Zhen–Hong Jia Gang Zhou Jia–Jia Wang School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory Xinjiang University Urumqi China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Vision-based insect detection is an important task in smart agriculture. Field-collected insect image datasets usually show severe imbalances in categories. Previous models have insufficient accuracy as well as low real-time performance for detecting unbalanced data of insects. To address the issues described above, an improved framework Balance Detect YOLO(BD-YOLO) based on YOLOv8 is proposed in this paper. We innovatively designed Focaler-CPIoU loss to increase the weight of a few classes of samples to achieve the optimisation of pest imbalance data detection. We introduced lightweight convolutional MBConv to improve the recognition accuracy while reducing the number of model parameters. In order to verify the effectiveness of the method in this paper, we established an insect dataset containing six types of insects, in which the most numerous thrips is 431.5 times more than the least numerous chrysopaperla. The experimental results show that compared with YOLOv8, the mAP50 reaches 61.7% with a 73.1% reduction in the number of model parameters, an improvement of 5.4%, and an improvement of 769 in FPS. A feasible approach to optimise the detection of unbalanced number of insects is provided in this paper along with the ability to be deployed on lightweight hardware devices.

关键词： YOLO Smart agriculture Accuracy Insects Data models Real-time systems Hardware Optimization Monitoring Cybernetics

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A Multi-grained based Attention Network for Semi-supervised Sound Event detection

arXiv

引用

arXiv 2022年

作者： Hu, Ying Zhu, Xiujuan Li, Yunlong Huang, Hao He, Liang Key Laboratory of signal detection and processing in Xinjiang China School of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University China

Sound event detection (SED) is an interesting but challenging task due to the scarcity of data and diverse sound events in real life. This paper presents a multi-grained based attention network (MGA-Net) for semi-supervised sound event detection. To obtain the feature representations related to sound events, a residual hybrid convolution (RH-Conv) block is designed to boost the vanilla convolution's ability to extract the time-frequency features. Moreover, a multi-grained attention (MGA) module is designed to learn temporal resolution features from coarse-level to fine-level. With the MGA module, the network could capture the characteristics of target events with short- or long-duration, resulting in more accurately determining the onset and offset of sound events. Furthermore, to effectively boost the performance of the Mean Teacher (MT) method, a spatial shift (SS) module as a data perturbation mechanism is introduced to increase the diversity of data. Experimental results show that the MGA-Net outperforms the published state-of-the-art competitors, achieving 53.27% and 56.96 % event-based macro F1 (EB-F1) score, 0.709 and 0.739 polyphonic sound detection score (PSDS) on the validation and public set respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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