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Attention-based Dual-Branch Network for Micro-Expression Recognition with Global-Local Feature Fusion 18

Attention-based Dual-Branch Network for Micro-Expression Rec...

引用

18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Qi, Yupeng Ibrayim, Mayire Hamdulla, Askar Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China

ISBN: (纸本)9798350364132

Micro-expression(ME) is an uncontrollable muscle movement that appears on the face when people try to hide or inhibit their real emotions, which has the problems of short duration, small movement amplitude and uneven distribution. In order to solve the problem of localization and asymmetry in the distribution of ME features, this paper proposes a new two-branch attention network to recognize MEs, which utilizes the attention mechanism to capture global and local ME features. The network is mainly divided into three parts: data preprocessing, ME feature learning and feature fusion classification. First, the data preprocessing first extracts the ME optical flow features and then divides them into four regions, which are used as inputs to the two-branch network respectively. Second, the two-branch network uses the Inception-MSFE global multi-scale feature extraction network incorporating the attention mechanism (CBAM) and the Swin Transformer-based local feature extraction network for feature learning, respectively. Finally, MEs are predicted by fusing global and local features of MEs. Experimental validation is carried out on three datasets, CASME II, SAMM, and SMIC, which proves that Acc, UAR, and UF1 are 0.797, 0.702, and 0.698 on the SAMM dataset, respectively;Acc, UAR, and UF1 are 0.734, 0.723, and 0.729 on SMIC, respectively;and on the CASME II dataset, Acc, UAR and UF1 are 0.865, 0.872, and 0.889, respectively, which are competitive with other state-of-the-art methods. © 2024 IEEE.

关键词： Feature extraction

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A Fiber Vibration signal Identification Method Based on the Combination of EWT-FE and LSTM

A Fiber Vibration Signal Identification Method Based on the ...

引用

2021 International Conference on Advanced Optics and Photonics Research in Engineering, AOPR 2021

作者： Zhang, Xin Ja, Qingmo Ruan, SaiSai Hu, Qin Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Xinjiang University Ürümqi830046 China

As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Wavelet transforms

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Language Identification Based on Residual Network and Attention Mechanism 3

Language Identification Based on Residual Network and Attent...

引用

3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Zeng, Ailing Ablimit, Mijit Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Language identification process is necessary for the downstream tasks of cross lingual and multi-lingual application. This paper proposes a language recognition method based on ResNet-CA-BiGRU-Attention model. Firstly, the speech signal is converted into the corresponding spectrogram, and then the residual network is used for feature extraction according to the distribution of speech energy on the spectrogram. Coordinate Attention and Attention are used to highlight corelated features and suppress irrelevant features. In this paper, five languages are selected from the Common Voice data set. Experimental results show that the proposed language recognition method based on ResNet-CA-BiGRU-Attention model can effectively improve the performance of language recognition. © 2022 IEEE.

关键词： Natural language processing systems

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Speech Separation and Emotion Recognition for Multi-speaker Scenarios 3

Speech Separation and Emotion Recognition for Multi-speaker ...

引用

3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Jin, Rong Ablimit, Mijit Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Emotion recognition in multi-speaker scenarios has been a hot topic of continuous research. In the single-channel overlapped speeches, the recognition accuracy is greatly degraded compared to clean speeches. Therefore, a speech separation task for multi-speaker separation at the front end of emotion recognition is necessary. The overall efficiency is the combined effect of separating a single speech from overlapped voices and emotional recognition. The speech separation experiments in this paper are based on Conv-TasNet, using the WSJ0-2mix dataset as training data to obtain the trained separation model;meanwhile, the speech emotion recognition experiments are inherited from Wav2vec-2.0 pre-trained model with a multi-task learning framework to recognize both text and emotion. Through the experiments, the possibility of bridging the two tasks and the usability of the separation and recognition results for realistic scenarios are demonstrated, and subsequent work can be carried out on improving the robustness and application to other realistic scenarios. © 2022 IEEE.

关键词： Emotion Recognition

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Language Identification in Overlapped Multi-lingual Speeches 3

Language Identification in Overlapped Multi-lingual Speeches

引用

3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Aysa, Zuhragvl Ablimit, Mijit Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Speech, languages, and background voices are overlapped in the multi-speaker environment. The speech separation process is necessary for downstream applications like language and speaker identification and ASR tasks. A combined speech separation and language recognition approach is proposed in this paper. First, the monolingual information is extracted from a multi-speaker multilingual speech scenario, then fed the separated speech to a language identification model. The speech separation model is trained in a single-channel overlapped speech data, and the language identification model is trained in Oriental language corpus. The combined effect of these two models is evaluated in the task of language identification. When the language recognition network is fed with the audio spectrograms separated by the source separation network, the Chinese and Korean language recognition results are significantly improved in terms of accuracy and recall rate than the mixed audio spectrograms. © 2022 IEEE.

关键词： Source separation

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Research on Text Recognition of Natural Scenes for Complex Situations 3

Research on Text Recognition of Natural Scenes for Complex S...

引用

3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Yu, Wenhua Ibrayim, Mayire Hamdulla, Askar Xinjiang University School of Information Science and Engineering Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

As a great invention that affects human development and progress, writing plays an essential role in human life and learning and the inheritance and development of culture. For thousands of years, the inheritance of Chinese culture has been inseparable from the writing records. Papermaking and printing have played a crucial role in the development of the times. Text extraction and recognition technology in natural scenes, that is, text recognition from photos or videos, has become a popular research direction in computer vision in recent years. Scene text recognition is a fundamental task in computer vision. It is often a critical step in many text-related tasks, such as document retrieval, card recognition, license plate recognition, and other tasks. The vital difference between the text recognition process and text detection is that the text in the photo or video needs to be recognized. detection and recognition are mainly divided into two steps: locating text areas in photos or videos, that is, finding words or text lines of the bounding box;then the positioned text is recognized. Combining these two steps results in End-to-end Recognition of text. The continuous development of scene text detection and recognition algorithm system will lay the foundation for the research of recognition problems such as multilingual recognition of scene text and formula recognition. © 2022 IEEE.

关键词： Computer vision

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Speech Language Identification Using CNN-BiLSTM with Attention Mechanism 3

Speech Language Identification Using CNN-BiLSTM with Attenti...

引用

3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Yilihamu, Dilizhati Ablimit, Mijit Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Aiming at the problems of low feature extraction ability and low recognition rate in language identification task, this paper proposes CNN-BiLSTM language identification method based on attention mechanism from the two aspects of feature and model. In the feature extraction stage, we compare the recognition effects of different speech features such as MFCC, LPCC and spectrogram, Experiments show that the recognition effect of spectrogram features is better in the actual environment. This method extracts the spectrogram of speech data, uses convolution network to obtain the visual features of spectrogram, and introduces attention mechanism, which can pay attention to the language related information in speech fragments and ignore non language information. This method is tested on two cross-channel data sets (AP19-olr) and (AP20-olr) of the Oriental Language Recognition competition. Compared with the language identification methods such as the baseline system x-vector, the equal error rate (EER%) on the two data sets is reduced by 3.38% and 4.66%, The results clearly show that the CNN-BiLSTM language identification method based on attention mechanism effectively improves the feature extraction ability in language identification task, and improves the performance of language identification in practical application environment. © 2022 IEEE.

关键词： Feature extraction

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Research on Speech Synthesis Based on Prosodic Controllability 5

Research on Speech Synthesis Based on Prosodic Controllabili...

引用

5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Gao, Wenyu Hamdulla, Askar Yang, Xipeng School of Information Science and Engineering Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang Urumqi China Mobvoi AI Lab Suzhou China

ISBN: (纸本)9798350337525

In today's highly interactive human-computer world, speech synthesis is widely used in many scenarios, and the requirements for rhyme effects in speech synthesis technology are increasing, so rhyme-controllable models have become a research hotspot. Most current rhyme controllable models are achieved by using a separate neural network to generate reference features, but this approach requires the training of more complex neural network models and the availability of reference audio to achieve display rhyme control. This paper proposes a rhyme-controllable solution based on an end-to-end acoustic model to address the problem of models being unable to precisely control the tone at the word level. The proposed model includes a tone-controllable module, which obtains duration information through the MFA alignment tool and adjusts the tone of the words by using word-level pitch control values and duration information. The acoustic model in this paper is improved by introducing pitch control during the generation of acoustic features and generating more robust audio by combining it with the decoder. In addition, to adjust the overall tone of the audio, a fixed coefficient is multiplied to the pitch values of all frames. Furthermore, this paper also proposes a 48KHz ultra-high-definition audio model by increasing the spectral parameter dimensions and upsampling by a factor. © 2023 IEEE.

关键词： Speech synthesis

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EEMD and Double Thresholds Integrated Voice Activity detection 3

EEMD and Double Thresholds Integrated Voice Activity Detecti...

引用

3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Meng, Shan Ablimit, Mijit Hamdulla, Askar Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang Urumqi China Xinjiang University Xinjiang Key Laboratory of Multilingual Information Technology School of Information Science and Engineering Xinjiang Urumqi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Voice activity detection (VAD) is an important preprocessing for voice applications. Anti-noise performance is the most important evaluation index of VAD algorithm. The traditional dual-threshold-based VAD algorithm has very low detection accuracy in a low signal-to-noise ratio environment. This paper proposes a voice activity detection algorithm based on Ensemble Empirical Mode Decomposition (EEMD) combined with the dual-threshold method, which integrates the decomposition of EEMD. The denoising feature is combined with VAD based on dual thresholds, and dual thresholds are set for VAD to improve the anti-noise performance and accuracy of the algorithm. VAD is divided into three categories: based on feature parameters, based on pattern recognition, and based on deep learning. The VAD algorithms based on pattern recognition and deep learning require the support of big training data to achieve good detection results. And it is too complex and requires a large amount of computation, thus its application and real-time deployment have been greatly affected. The traditional VAD methods based on feature parameters only needs to use the short-term energy and short-term zero-crossing rate as the judgment criteria for activity detection. Thise algorithm is simple, easy to deploy, and applicable even if it is dozens of voices. At the same time, the EEMD changes the extreme point characteristics of the signal by adding different white noises of the same amplitude each time, and then performs the overall average of the corresponding IMF(intrinsic mode function) obtained by multiple EMDs to offset the added white noise, therefor effectively suppressing the mode-mixing. Thus the production of state aliasing can better improve the anti-noise performance of the VAD algorithm. © 2022 IEEE.

关键词： Empirical mode decomposition

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Research of Scene Text detection Algorithms 5

Research of Scene Text Detection Algorithms

引用

5th International Conference on Intelligent Robotics and Control Engineering, IRCE 2022

作者： Chen, Mengmeng Ibrayim, Mayire Hamdulla, Askar School of Information Science and Engineering Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang Urumqi China School of Information Science and Engineering Xinjiang University Xinjiang Key Laboratory of Multilingual Information Technology Xinjiang Urumqi China

ISBN: (数字)9781665469951

ISBN: (纸本)9781665469951

With the development of artificial intelligence, obtaining textual information from natural scenes has become a hot topic. There are still huge challenges for curved text and arbitrary orientation text detection in real-world scenes. In this work, we briefly introduce the development of text detection algorithms in recent years and study a typical lightweight detection algorithm. We have improved the algorithm to different degrees to make it more suitable for detection of the curved text. Finally, we select the three most representative public text datasets for experiments. Experiments conducted on CTW1500, ICDAR2015, and MSRA-TD500 have demonstrated the superiority of this model. © 2022 IEEE.

关键词： Deep learning

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