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2024 Biomedical Circuits and Systems Conference

作者： Zhai, Jinglei Zhao, Wenfeng Sun, Biao Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China SUNY Binghamton Dept Elect & Comp Engn Binghamton NY USA

ISBN: (纸本)9798350354966;9798350354959

Respiratory diseases are one of the most common causes of death worldwide. At present, digital stethoscopes are valuable tools for diagnosing respiratory diseases, but they face limitations in storage, computation, and transmission capabilities. Therefore, developing respiratory sound compression algorithms is crucial. In this work, we propose TranscoderQVAE (TQVAE). TQVAE adopts the idea of variational autoencoder, non-uniform quantizer and Transformer to perform compression and recovery tasks on respiratory sounds Based on the concept of compressive sensing, we use an encoder to reduce data points and a non-uniform quantizer to compress the signal to 4 bits and stored which can reduce the storage space. Finally, we use a simplified Transformer as a decoder named Transcoder to recover signals. Meanwhile, a denoising algorithm based on wavelet transformation is applied to the recovered signal to remove noise. Through performing experiments of 3554 pieces of data, the correlation coefficient between the recovered signal and the raw signal can reach 0.98. And the compression ratio can reach up to 256. Other evaluation metrics are also terrific. The algorithm execution time is less than 13 seconds per signal.

关键词： respiratory sounds compressive sensing variational autoencoder non-uniform quantizer Transformer network

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Mapping the Audio Landscape for Innovative Music Sample Generation 24

Mapping the Audio Landscape for Innovative Music Sample Gene...

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4th Annual International Conference on Multimedia Retrieval (ICMR)

作者： Limberg, Christian Zhang, Zhe Natl Inst Informat Chiyoda Ku Tokyo Japan

ISBN: (纸本)9798400706028

This paper introduces the Generative Sample Map (GESAM), a novel two-stage unsupervised learning framework capable of generating high-quality and expressive audio samples for music production. Recent generative approaches based on language models rely on text prompts as conditions. However, fine nuances in musical audio samples can hardly be described in the modality of text. For addressing this shortcoming, we propose to learn a highly descriptive latent 2D audio map by a variational autoencoder (VAE) which is then utilized for conditioning a Transformer model. We demonstrate the Transformer model's ability to achieve high generation quality and compare its performance against two baseline models. By selecting points on the map that compresses the manifold of the audio training set into 2D, we enable a more natural interaction with the model. We showcase this capability through an interactive demo interface, which is accessible on our website https://***/gesam/.

关键词： Audio Generation Generative Models Visualization Audio Visualization User Interfaces Transformer variational autoencoder

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Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer

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FUTURE ONCOLOGY 2023年第20期19卷 1429-1438页

作者： Li, Xue Yang, Lifeng Jiao, Xiong Taiyuan Univ Technol Coll Biomed Engn Jinzhong 030600 Shanxi Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Jinzhong 030600 Shanxi Peoples R China

Aim: To develop a deep learning-based multiomics integration model. Materials & methods: Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model via a deep neural network, incorporating an attention mechanism that adaptively considers the weights of multiomics features. Results: Compared with other methods, the deep learning-based multiomics integration model achieved remarkable results, with an area under the curve of 0.89 (95% CI: 0.863-0.910). Conclusion: The deep learning-based multiomics integration model achieved promising results and is an effective method for predicting axillary lymph node metastasis in breast cancer.

关键词： axillary lymph node metastasis breast cancer deep learning multiomics variational autoencoder

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ECG-grained Cardiac Monitoring Using RFID 33

ECG-grained Cardiac Monitoring Using RFID

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33rd IEEE International Conference on Computer Communications and Networks (ICCCN)

作者： Wang, Ningning Zhao, Tianya Mao, Shiwen Bai, Harrison X. Jiao, Zhicheng Wang, Xuyu Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA Johns Hopkins Univ Dept Radiol & Radiol Sci Sch Med Baltimore MD 21287 USA Rhode Isl Hosp Dept Diagnost Imaging Providence RI 02903 USA

ISBN: (纸本)9798350348439;9798350384611

Heartbeat signals are useful to disease prediction, sub-health diagnosis, fatigue warning, and even emotion estimation. There is a compelling need for contactless, easy-to-deploy, and long-term heartbeat monitoring. This paper presents a contactless Radio Frequency Identification (RFID) based system for heartbeat monitoring that leverages the insight that RFID signal fluctuations induced by chest motion are synchronous with both respiration and heartbeat. The proposed system collects the temporal phase information from the tag pair on the body to extract heartbeat signals using a sequence of signal processing techniques. We propose a signal separation method based on empirical mode decomposition (EMD) to obtain heart rate after preprocessing. Furthermore, the estimated signal is input to an enhanced variational autoencoder (VAE) model to recover the heartbeat waveform. Implemented with commercial off-the-shelf (COTS) RFID devices, the system achieves accurate heart rate monitoring with less than 3% relative errors. The detected waveform exhibits a median cosine similarity of 0.83 as compared with the ground truth, which validate the system's wide applicability and high reliability for fine-grained, contactless heartbeat monitoring.

关键词： Radio Frequency Identification (RFID) vital sign monitoring contact-free sensing variational autoencoder

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Deep Learning Enabled Bandpass Electromagnetic Metasurface Inverse Design

Deep Learning Enabled Bandpass Electromagnetic Metasurface I...

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2024 Cross Strait Radio Science and Wireless Technology Conference

作者： Zhao, Shenghui Zhang, Ruizhi Guo, Meng Zhang, Xuewei Nie, Lifeng Pang, Xiaoyu Res Inst Special Structures Aeronaut Composite AV Jinan 250023 Shandong Peoples R China Qilu Univ Technol Shandong Acad Sci Natl Supercomput Ctr JinanKey Lab Comp Power Net Shandong Comp Sci CtrMinist Educ Jinan 250013 Shandong Peoples R China

ISBN: (纸本)9798331507800;9798331507794

Conditional variational autoencoders (cVAE) is proposed to automate and optimize the design of band-pass electromagnetic metasurfaces. Electromagnetic responses curves and metasurface structural parameters are used as the input to train the deep neural work based cVAE model. The model is trained on ten thousand metasurface and transmission curve pairs, which are calculated using MIT Electromagnetic Equation Propagation (MEEP) software. The trained model takes transmission curves as input and then output metasurface structures, achieving a deep learning enabled inverse design process. By comparing the input transmission curve with the MEEP simulated curves of the generated metasurface, it is found that the model has high accuracy, giving a mean square error (MSE) value of 0.005 and R2 score of 0.967. The metasurface structures generated by cVAE also show good diversity, indicating that the cVAE captures the effective features in the latent space. Using the cVAE model, metasurface could be generated in less than one second using a single graphics processing unit (CPU), paving the way for fast, efficient and scalable inverse design solutions in the field of electromagnetic metasurfaces.

关键词： metasurface frequency selective surface deep learning variational autoencoder

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LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any Singing Voice Conversion with Singer Guidance 25

LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any S...

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25th Interspeech Conference

作者： Chen, Shihao Gu, Yu Zhang, Jie Li, Na Chen, Rilin Chen, Liping Dai, Lirong Univ Sci & Technol China USTC NERC SLIP Hefei Peoples R China Tencent AI Lab Shenzhen Peoples R China

Any-to-any singing voice conversion (SVC) is an interesting audio editing technique, aiming to convert the singing voice of one singer into that of another, given only a few seconds of singing data. However, during the conversion process, the issue of timbre leakage is inevitable: the converted singing voice still sounds like the original singer's voice. To tackle this, we propose a latent diffusion model for SVC (LDM-SVC) in this work, which attempts to perform SVC in the latent space using an LDM. We pretrain a variational autoencoder structure using the noted open-source So-VITS-SVC project based on the VITS framework, which is then used for the LDM training. Besides, we propose a singer guidance training method based on classifier-free guidance to further suppress the timbre of the original singer. Experimental results show the superiority of the proposed method over previous works in both subjective and objective evaluations of timbre similarity.

关键词： Singing voice conversion latent diffusion model variational autoencoder classifier-free guidance

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Detailed and Controllable Old Photo Restoration with Diffusion Priors 19

Detailed and Controllable Old Photo Restoration with Diffusi...

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19th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Liu, Xibei Wang, Han Wang, Yiwen Zhang, Yuhong Song, Jiayi Xie, Rong Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Amer Straight A Acad Dallas TX USA Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr CMIC Shanghai Peoples R China

ISBN: (纸本)9798350364279;9798350364262

Restoring old photos that contain numerous unknown and complex defects is a challenging and ill-posed problem. Traditional methods often struggle to address both structured and unstructured defects in real old photos, frequently leading to over-smoothed and uncompleted results. In this paper, we exploit powerful diffusion priors to construct a novel solution for the restoration of old photos. Our framework begins with a coarse stage model aimed at eliminating unstructured defects, followed by a conditional diffusion model that further refines the content and enriches details. Specifically, we introduce an edge control module, designed to encode the restored edge map into the denoising network. This integration effectively guides the restoration process, allowing the edge conditional diffusion to achieve more precise and controllable results. Additionally, we incorporate a feature fusion VAE to ensure the fidelity of the final outputs. Through extensive qualitative, quantitative, and ablative experiments, we demonstrate the innovation and effectiveness of the proposed method, which offers a complete, detailed, and personalized restoration of old photos.

关键词： Old Photo Restoration Edge Condition variational autoencoder Diffusion Model

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Enhancing Bandwidth Efficiency for Video Motion Transfer Applications Using Deep Learning Based Keypoint Prediction 25th

Enhancing Bandwidth Efficiency for Video Motion Transfer App...

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25th International Conference on Engineering Applications of Neural Networks (EANN)

作者： Bai, Xue Haque, Tasmiah Mohan, Sumit Cai, Yuliang Jeong, Byungheon Halasz, Adam Das, Srinjoy West Virginia Univ Morgantown WV 26506 USA Intel Corp Santa Clara CA USA Univ Southern Calif Los Angeles CA 90089 USA Coupa Software San Mateo CA USA

ISBN: (纸本)9783031624940;9783031624957

We propose a deep learning based novel prediction framework for enhanced bandwidth reduction in motion transfer enabled video applications such as video conferencing, virtual reality gaming and privacy preservation for patient health monitoring. To model complex motion, we use the First Order Motion Model (FOMM) that represents dynamic objects using learned keypoints along with their local affine transformations. Keypoints are extracted by a self-supervised keypoint detector and organized in a time series corresponding to the video frames. Prediction of keypoints, to enable transmission using lower frames per second on the source device, is performed using a variational Recurrent Neural Network (VRNN). The predicted keypoints are then synthesized to video frames using an optical flow estimator and a generator network. This efficacy of leveraging keypoint based representations in conjunction with VRNN based prediction for both video animation and reconstruction is demonstrated on three diverse datasets. For real-time applications, our results show the effectiveness of our proposed architecture by enabling up to 2x additional bandwidth reduction over existing keypoint based video motion transfer frameworks without significantly compromising video quality.

关键词： Motion transfer Bandwidth reduction Recurrent Neural Network variational Recurrent Neural Network variational autoencoder Keypoints

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3D Artistic Images Generation from 2D Images Using Improved Wasserstein Generative Adversarial Networks 8th

3D Artistic Images Generation from 2D Images Using Improved ...

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2024 World Congress in Computer Science, Computer Engineering and Applied Computing-CSCE

作者： Esan, Dorcas Oladayo Owolawi, Pius Adewale Tu, Chunling Tshwane Univ Technol Dept Comp Syst Engn Pretoria South Africa

ISBN: (纸本)9783031856273;9783031856280

Excellent artistic work frequently showcases the individual inventiveness of the creator, and many artists place a high value on producing images of superior quality. A remarkable success has been achieved by GANs in producing photorealistic 2-D images. Because traditional GANs generate 3D images directly from 2D, their images frequently lack the perfect quality and consistency of multiple views from a single image. With varied degrees of success, numerous attempts have been made to address these issues. With the use of a 3D variational autoencoder (VAE) that integrated Improved Wasserstein Generative Adversarial Networks (3D VAE-IWGAN), this research aims to produce high-quality and consistent multi-view three-dimensional (3D) artistic images. The experiments were conducted using the COCO Africa Mask dataset, and the suggested method produced high-quality images that were more closely aligned with the aesthetic qualities of authentic works of art than other complex methods. In comparison to other methods, the proposed method has a high Inception Score(IS) score of 26.37, a low frechet inception distance (FID) score of 12.09, a peak signal-to-noise ratio (PSNR) of 31.35, a mean squared error (MSE) of 65.32, a chamfer distance (CD) of 0.243, Learned Perceptual Image Patch Similarity (LPIPS) score of 0.134 and a structural similarity index measure (SSIM) of 0.772. These results are better than those obtained with other methods on the COCO Africa Mask dataset. The suggested approach can help artists showcase, develop, and improve the originality of their work, which will increase their financial gains.

关键词： Generative Adversarial Networks 3-D Image variational autoencoder artistic Image Improved Wasserstein GAN

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Balancing Class Distribution in Microarray Analysis Using GenAI 4th

Balancing Class Distribution in Microarray Analysis Using Ge...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Anurekha, G. Amutha, S. Nivethika, K. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9783031821554;9783031821561

Microarrays are sophisticated datasets that have a substantial number of features and a limited number of samples. This can lead to a class imbalance problem. Balancing class distribution is an important research domain. In this paper, a novel variational auto encoder model is proposed for class imbalance mitigation. The proposed model employs an adversarial loss function to generate simulated data. The proposed model is evaluated using the autism gene expression dataset. The obtained experimental results prove that the data generated from the proposed model have obtained noteworthy results compared with the original dataset by employing three different classifiers.

关键词： Class imbalance Deep learning Microarray Simulated data variational autoencoder

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