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A CRNN-GCN Piano Transcription Model Based On Audio And Skeleton Features

A CRNN-GCN Piano Transcription Model Based On Audio And Skel...

引用

Acoustics, Speech, and Signal processing Workshops (ICASSPW), ieee International conference on

作者： Yuqing Li Xianke Wang Ruimin Wu Wei Xu Wenqing Chen School of Electronic Information and Communications Hubei Provincial Key Laboratory of Smart Internet Technology Huazhong University of Science and Technology Wuhan China

Piano transcription is a fundamental problem in music information retrieval, which aims to infer the note sequence from the piano multimedia. This paper proposes a piano transcription model named CRNN-GCN, which is fused with audio transcription model Onset and Frames(CRNN) and visual transcription model Graph Convolutional Network(GCN). CRNN extracts features from the audio, and GCN extracts features from hand skeletons rather than video frames, which effectively reduces video memory and computational complexity. All the features are then integrated to obtain better transcription results. On our self-built dataset OMAPS2, the F1-scores of the single-modal CRNN and GCN are 89.98% and 61.63%, while the F1-score of the multi-modal CRNNGCN reaches 92.06%, which is the best result at present.

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Using multi-task residual network to evaluate image aesthetic quality 5

Using multi-task residual network to evaluate image aestheti...

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5th ieee Advanced Information Technology, Electronic and Automation Control conference, IAEAC 2021

作者： Tian, Xiuling Animation and Communication College of Qingdao Agricultural University Qingdao China

ISBN: (纸本)9781728180281

With the popularity of cameras, smartphones and other devices, visual content data such as images and videos are increasing day by day, and visual content data processing technology has become a research direction in the research fields of computer vision, computational photography and artificial intelligence. Compared with the semantic features of object recognition, the mystery of human aesthetics has not yet been quantified and scientifically explained. It is difficult to determine the feature points of image aesthetics, which increases the difficulty of image aesthetics quality evaluation. In order to better obtain the feature points of image aesthetics, this paper uses a multi-task residual network model to extract features of the image, and evaluate the aesthetic level of the image according to the extracted features. Using a special aesthetic evaluation data set to test and verify the accuracy of the evaluation model proposed in this paper is 84.68%, which can more accurately evaluate the aesthetic level of images. Therefore, the evaluation model proposed in this paper promotes the development of image aesthetic value evaluation technology. © 2021 ieee.

关键词： Artificial intelligence

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Designing Individual Optimistic and Pessimistic Emotional Tendency Identification System Based on Digital image processing

Designing Individual Optimistic and Pessimistic Emotional Te...

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2021 ieee International conference on Industry 4.0, Artificial Intelligence, and communications Technology, IAICT 2021

作者： Akbar, Andi Fatahillah Fauzi, Hilman Aulia, Pramitha Yora, Utari Nur Ramadhani Faculty of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9781665444941

Handwriting analysis, commonly referred to as Graphology, can reflect a person's personality because writing movements are controlled by the brain, which contains memories about various life experiences and stored in the subconscious. Currently, the process of identifying human personality through handwriting or Graphology is still performed manually. This process requires a reference book to analyze every aspect of a person's handwriting. As well as the baseline pattern of handwriting, still performed manually to decide whether it tends to be up, down, or straight. In this paper, the aspects studied were the primary writing lines to identify a person's personality traits and characteristics towards emotional individuals with optimistic and pessimistic characters. The test is carried out using the method classification of the ArcTan geometric formula to determine the angle of slanted of the line basic handwriting. System inputs were using handwriting samples obtained from 42 subjects, ranging from 19-27 years old. The system was designed to identify two classes of emotions, which are optimistic and pessimistic. Then three essential line aspects of handwriting, namely tend up, tend down, and straight, were classified according to the arctan geometric formula. The accuracy of this graphology system is 90.47%;it can be concluded that the system successfully identifies handwriting per 1 line or 1 page of HVS paper. © 2021 ieee.

关键词： image processing

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Improvement of the Algorithm for Nonlinear Adaptive image Contrast Enhancement Based on the Theory of Fuzzy Sets

Improvement of the Algorithm for Nonlinear Adaptive Image Co...

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2021 International conference on Information Science and communications Technologies, ICISCT 2021

作者： Iskandarova, Sayyora Boynazarov, Ilhom University of Information Technologies Samarkand Branch of Tashkent 47A Shokhrukh Mirzo str. Samarkand Uzbekistan

ISBN: (纸本)9781665432580

Currently, primary image processing is used to improve recognition results. In this paper, improvements have been performed using normalization and phasing operations to increase image contrast. A normalized histogram has been also built. Mathematical expectations have been built for normalized and fuzzy image contrast enhancements. Shannon's entropy has been represented as fuzzy. Results of image sharpening have been presented and advantages over other algorithms have been indicated in the paper. © 2021 ieee.

关键词： Fuzzy sets

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Application of Computer Digital Simulation Technology in the Research of Martial arts Action Structure image 16

Application of Computer Digital Simulation Technology in the...

引用

16th ieee International Wireless communications and Mobile Computing conference (ieee IWCMC)

作者： Wang, Qingbao Baicheng Normal Univ Coll Phys Educ Baicheng 137000 Jilin Peoples R China

ISBN: (纸本)9781728131290

The reference of Martial arts action structure to assist the artificial correction is poor, for this problem, in this paper, a new method of Martial arts action structure analysis and reconstruction based on computer numerical simulation is proposed. Firstly, three-dimensional visual image information acquisition of martial arts is carried out, and adaptive threshold decomposition and wavelet analysis are used to perform noise reduction pretreatment. The original domain feature point library is formed by the original separation of the domain feature points of the martial arts action structure image. Then, the edge contour feature extraction method is used to extract the contour features of the martial arts in the image, input into the expert system of the orthodontics, carry on the visual analysis and correction, and realize the Martial arts action structure analysis and the feature reconstruction identification. The results of computer simulation show that this method can be used to reconstruct the martial arts structure and improve the quantitative analysis ability of martial arts. The signal-to-noise ratio of the output image is high, and the recognition performance of the image processing and motion is better.

关键词： computer numerical simulation technology Martial arts sports training structural characteristics action identification

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An Adapted Mean Teacher Model for Semi-supervised Indoor Positioning using Channel Impulse Response

An Adapted Mean Teacher Model for Semi-supervised Indoor Pos...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and communications (GreenCom)

作者： Yingzhi Liu Bei Yang Wei Li Xiaoming She Gang Feng Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China 6G Research Center China Telecom Research Institute Beijing China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu China

As an effective solution to alleviating the insufficiency of labeled data for indoor positioning, deep semi-supervised learning (DSSL) can be employed to lessen the dependency on labeled data by exploiting potential patterns in unlabeled samples. Inspired by the inherent similarity between image processing and indoor positioning, and the efficiency of the consistency regularization method for image classification, we propose an adapted mean teacher (AMT) model under the DSSL paradigm for indoor positioning by using channel impulse response. For enhancing the generalization of the trained model, we design an efficient implicit augmentation scheme for the training process in AMT model. In addition, we design a special residual network to efficiently extract location characteristics in the AMT framework. We conduct extensive simulation experiments for indoor scenarios with a heavy non-line-of-sight condition to demonstrate the effectiveness of our proposed AMT model. Numerical results illustrate that AMT model outperforms a number of consistency regularization methods and the pseudo-label method, in terms of accuracy and convergence.

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CSC-Net: A Lightweight Model for Remote Real-Time Monitoring of Infrared Small Targets

CSC-Net: A Lightweight Model for Remote Real-Time Monitoring...

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Future communications and Networks (FCN), International conference on

作者： Mingjin Zhang Nan Zhou Xiaofeng Li Xiaogang Dong Luchang Yang Yunsong Li Xidian University Xi’an China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

Infrared small target detection (IRSTD) is an important subject in many fields such as real-time monitoring and drone applications. Drones typically require real-time processing and transmission of infrared image data during flight. Lightweight models can reduce computational and communication burdens. While existing methods perform target detection effectively, resource-intensive demands hinder remote real-time detection. To provide high-performance target detection and analysis in resource-constrained environments, we propose a cross-sensing compression network (CSC-Net) for IRSTD in this paper. Specifically, the compression mixer module (CMM) enhances the hybrid scheme’s adaptability to different locations by exploring both semantic and spatial dimensions, thus improving image association flexibility. The filter perception module (FPM) dynamically adjusts input contributions for enhanced cross-sensing, emphasizing the benefits of multilayered semantics. Experiments on SIRST and IRSTD-1K show that the proposed CSC-Net can achieve accurate target location and segmentation. In particular, its visual effects and resource consumption can meet the remote real-time monitoring scenario.

关键词： Adaptation models image segmentation image coding Computational modeling Semantics Object detection visual effects

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Extracting the Brain-Like Representation by an Improved Self-Organizing Map for image Classification

Extracting the Brain-Like Representation by an Improved Self...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jiahong Zhang Lihong Cao Moning Zhang Wenlong Fu State Key Laboratory of Media Convergence and Communication Communication University of China Neuroscience and Intelligent Media Institute Communication University of China Beijing China

Backpropagation-based supervised learning has achieved great success in computer vision tasks. However, its biological plausibility is always controversial. Recently, the bioinspired Hebbian learning rule (HLR) has received extensive attention. Self-Organizing Map (SOM) uses the competitive HLR to establish connections between neurons, obtaining visual features in an unsupervised way. Although the representation of SOM neurons shows some brain-like characteristics, it is still quite different from the neuron representation in the human visual cortex. This paper proposes an improved SOM with multi-winner, multi-code, and local receptive field, named mlSOM. We observe that the neuron representation of mlSOM is similar to the human visual cortex. Furthermore, mlSOM shows a sparse distributed representation of objects, which has also been found in the human inferior temporal area. In addition, experiments show that mlSOM achieves better classification accuracy than the original SOM and other state-of-the-art HLR-based methods. The code is accessible at https://***/JiaHongZ/mlSOM.

关键词： Self-organizing feature maps visualization Neurons Supervised learning Signal processing Encoding Task analysis

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A Seq2Seq-LSTM-Attention Model for Ionospheric foF2 Prediction in the Middle Latitude Region

A Seq2Seq-LSTM-Attention Model for Ionospheric foF2 Predicti...

引用

ieee International conference on Signal and image processing (ICSIP)

作者： Zhenhai Lu Kun Xu Hansheng Wang Ke Du College of Information and Communication National University of Defense Technology Wuhan China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Predicting the critical ionospheric frequency of F2 layer (foF2) could provide guidance for satellite navigation and high frequency communications frequency selection. Models based on deep learning have been proven to forecast ionospheric variations effectively. In this letter, we propose a Seq2Seq model with long short-term memory and attention mechanism (Seq2Seq-LSTM-Attention), aiming at predicting the foF2 parameter more accurately. The training and testing of the foF2 measurements from Wuhan, China (30.6°N, 114.3°E) show that the proposed Seq2Seq-LSTM-Attention model can effectively capture the correlation in the foF2 sequences and outperforms several existing cutting-edge deep learning based models in prediction accuracy. The results confirm that the proposed model is able to mine the potential relationships in the foF2 sequences more deeply.

关键词： Deep learning Training Accuracy Correlation Attention mechanisms Predictive models Signal processing

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Chambolle’s Projection-based Total Variation Deepnet Layer for image Denoising

Chambolle’s Projection-based Total Variation Deepnet Layer ...

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ieee International conference on Intelligent Computer Communication and processing (ICCP)

作者： George Florin Grosu Romulus Terebes Communications Department Technical University of Cluj-Napoca Cluj-Napoca Romania

This research paper presents the implementation and evaluation of a Total Variation (TV) layer within a deep learning framework for image denoising tasks. The TV layer is based on Chambolle’s projection method and aims to enhance the performance of a baseline denoising network. The study investigates the effectiveness of incorporating a single TV denoising layer, which adds only six parameters to an arbitrary baseline autoencoder. The resulting models are evaluated across three datasets with different Gaussian noise. By comparing an arbitrary network architecture with an identical counterpart model that incorporates the TV denoising layer, the results indicate significant improvements in performance. The magnitude of these performance gains diminishes as the variance of noise increases. Notably, for lower Gaussian noise variance such as for a standard deviation of 25, the gain in terms of the mean peak signal-to-noise ratio (PSNR) metric is estimated to be approximately +0.89 dB, a discernible improvement that is visually perceptible. Although the resulting model has no significant increase in terms of model size, it does present significant increases in complexity.

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