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VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis

VoiceDiT: Dual-Condition Diffusion Transformer for Environme...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Jaemin Jung Junseok Ahn Chaeyoung Jung Tan Dat Nguyen Youngjoon Jang Joon Son Chung Korea Advanced Institute of Science and Technology South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We present VoiceDiT, a multi-modal generative model for producing environment-aware speech and audio from text and visual prompts. While aligning speech with text is crucial for intelligible speech, achieving this alignment in noisy conditions remains a significant and underexplored challenge in the field. To address this, we present a novel audio generation pipeline named VoiceDiT. This pipeline includes three key components: (1) the creation of a large-scale synthetic speech dataset for pre-training and a refined real-world speech dataset for fine-tuning, (2) the Dual-DiT, a model designed to efficiently preserve aligned speech information while accurately reflecting environmental conditions, and (3) a diffusion-based image-to-Audio Translator that allows the model to bridge the gap between audio and image, facilitating the generation of environmental sound that aligns with the multi-modal prompts. Extensive experimental results demonstrate that VoiceDiT outperforms previous models on real-world datasets, showcasing significant improvements in both audio quality and modality integration. Synthesized samples are available on our demo page: https://***/projects/voicedit/

关键词： Adaptation models Visualization Translation Pipelines Production Speech enhancement signal processing Transformers Acoustics Text to speech

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Integrated image Processing Device For Visually Impaired

Integrated Image Processing Device For Visually Impaired

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international conference on Communication, Computing and internet of Things (IC3IoT)

作者： J. Raja N Kaviya B V Koushika R Sushma Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai Tamil Nadu

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

In a world where technology is developing more quickly than ever before, many of these advances have been aimed at making people's lives easier. To aid with these efforts, we are creating an integrated picture processor for visually impaired people. The main forms of human communication nowadays are speech and text. A person must have the vision to view text-based information. Nonetheless, persons without the ability to see can still learn things by listening. The integrated image processor is an assistive text-reading tool that uses a camera to help the blind read text on labels, printed notes, and merchandise. It entails text extraction from an image using optical character recognition (OCR) and text-to-speech (TTS) conversion to turn it into speech. This method aids the blind in reading the text and serves as the foundation for the creation of a prototype that will enable the blind to identify objects in the real world. Using Jetson Nano, the text from product descriptions is retrieved and rendered as speech, with mobility as the primary consideration. It entails text extraction from an image using optical character recognition (OCR) and text-to-speech (TTS) conversion to turn it into speech. This method aids the blind in reading the text and serves as the foundation for the creation of a prototype that will enable the blind to identify objects in the real world. Using Raspberry Pi, the text from product descriptions is retrieved and rendered as speech, with mobility as the primary consideration. By including a battery backup, portability is made possible and could be used in future technology. The user can use the device at any time and any place because of its portability. The project also has a feature that uses OpenCV and TensorFlow to recognize cash notes. Additionally, this system incorporates deep learning-based object identification, which uses MobileNets and SSD methods for object detection, to recognize various items for recognizing barriers in front of

关键词： image processing ieee merchandise Optical character recognition Prototypes Speech recognition Object detection Cameras

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Multi-Modal Domain Generalization for Cross-Scene Hyperspectral image Classification

Multi-Modal Domain Generalization for Cross-Scene Hyperspect...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Yuxiang Zhang Mengmeng Zhang Wei Li Ran Tao School of Information and Electronics Beijing Institute of Technology Beijing Key Lab of Fractional Signals and Systems

The large-scale pre-training image-text foundation models have excelled in a number of downstream applications. The majority of domain generalization techniques, however, have never focused on mining linguistic modal knowledge to enhance model generalization performance. Additionally, text information has been ignored in hyperspectral image classification (HSI) tasks. To address the aforementioned shortcomings, a Multi-modal Domain Generalization Network (MDG) is proposed to learn cross-domain invariant representation from cross-domain shared semantic space. Only the source domain (SD) is used for training in the proposed method, after which the model is directly transferred to the target domain (TD). Visual and linguistic features are extracted using the dual-stream architecture, which consists of an image encoder and a text encoder. A generator is designed to obtain extended domain (ED) samples that are different from SD. Furthermore, linguistic features are used to construct a cross-domain shared semantic space, where visual-linguistic alignment is accomplished by supervised contrastive learning. Extensive experiments on two datasets show that the proposed method outperforms state-of-the-art approaches.

关键词： Training Visualization Semantics Linguistics Feature extraction Transformers Generators

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VMAF based quantization parameter prediction model for low resolution video coding 15

VMAF based quantization parameter prediction model for low r...

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15th international conference on Advanced Technologies for Communications, ATC 2022

作者： Vu, Tien Huu Cong, Huy Phi Sisouvong, Thipphaphone Hoangvan, Xiem Nguyenquang, Sang Dongoc, Minh Posts and Telecommunications Institute of Technology Hanoi Viet Nam University of Engineering and Technology Vietnam National University Hanoi Viet Nam

ISBN: (数字)9781665451888

ISBN: (纸本)9781665451888

Perceptual video quality assessment (VQA) is now essential part of various video applications by controlling the quality for the videos delivered to the users. Recently, Video Multimethod Assessment Fusion (VMAF) is developed by Netflix, known as a full refence VQA model that combines spatial and temporal features to predict perceptual quality. This metric has stronger correlation with human visual system than the conventional metrics such as Peak signal to Noise Ratio (PSNR) and Structure Similarity Index (SSIM). However, the perceptual video quality is normally degraded due to the content and bitrate variation. This leads to uncomfortable experience for the viewers. In this context, this paper proposes an adaptive quantization parameter (QP) prediction model based on VMAF for video coding to achieve better perceptual quality at a reasonable bitrate. In particular, the Rate Distortion Optimization (RDO) progress of x.264 video codec is improved by using the VMAF scores to find optimal QP map. The results showed that the compression performance of x.264 codec with the proposed method achieved up to 5.95% bitrate saving when compared to the conventional x.264 encoder. © 2022 ieee.

关键词： image coding

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Radar signal Modulation Recognition based on Improved Instantaneous Autocorrelation 6

Radar Signal Modulation Recognition Based on Improved Instan...

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6th international conference on signal and image Processing, ICSIP 2021

作者： Wang, Guotao Jiang, Qiuxi Sun, Bing National University of Defense Technology Institute of Electronic Countermeasure Hefei China

ISBN: (纸本)9780738133737

Radar signal recognition is one of the kernel aspects of the radar emitter recognition process. In order to realize the fast and efficient recognition of intra-pulse modulation of radar signal, this paper introduces a method based on improved instantaneous autocorrelation. Firstly, the signal is roughly divided into phase modulation signals and frequency modulation signals according to the bandwidth. and then the instantaneous autocorrelation feature of the signal is extracted by the improved method. Finally, the frequency modulation and phase modulation signal types are subdivided based on the amplitude and phase characteristics of instantaneous autocorrelation. Simulation results show that the method has low computational complexity and good recognition success rate. © 2021 ieee.

关键词： Autocorrelation

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Assessment of Nonidentical Flight Passes on Wavelength-Resolution SAR Change Detection based on Tensor Robust PCA

Assessment of Nonidentical Flight Passes on Wavelength-Resol...

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ieee international conference on Radar

作者： Lucas P. Ramos Dimas I. Alves Renato Machado Leonardo T. Duarte Viet. T. Vu Mats Pettersson Department of Telecommunications Aeronautics Institute of Technology São José dos Campos Brazil School of Applied Sciences University of Campinas Limeira Brazil Department of Mathematics and Natural Sciences Blekinge Institute of Technology Karlskrona Sweden

ISBN: (数字)9798331539566

ISBN: (纸本)9798331539573

Change detection methods are, usually, restricted to SAR image pairs or stacks from identical flight geometries. This selection is mainly explained by the fact that the specular reflection of each object on the ground, such as vehicles, power lines, and buildings, depends on the incident angle and, therefore the heading angle of the platform in a pass. Thus, non-identical acquisitions can significantly increase the number of false alarms for change detection methods. This paper assesses non-identical flight passes on change detection based on tensor robust principle component analysis (TRPCA). We have considered the wavelength-resolution SAR images from the CARABAS-II data set for this assessment. These SAR images are well known for their stability and for being unaffected by speckle noise, thanks to the system resolution in the order of the radar signal wavelength. The experiments in four cases provided in the paper, two cases using images with identical passes and two cases using non-identical passes, have shown that TRPCA can perform very well change detection even in the scenarios where a surveillance image was acquired with a flight pass different from the ones used for the reference images. In addition, the results have shown that the most significant contribution of false alarms comes from elongated structures that can be sensitive to a flight pass but insensitive to others. Such elongated structures can be removed through post-processing techniques.

关键词： Tensors Surveillance Radar detection Radar Radar imaging Speckle Radar polarimetry Stability analysis signal resolution Principal component analysis

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Estimating of Noise Patterns by Satellite image using ELBP and SVM

Estimating of Noise Patterns by Satellite Image using ELBP a...

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2022 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Sabareeswari, T.C. Hanusha, A. Vamsi, Paturi Kumar, Pudi Tharun Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350333848

In order to categorize satellite photos into a collection of 24 distinct classes, Support vector machines (SVM) and Extended Local Binary Patterns (EL-BP) approaches based on machine learning have been utilized. This work classifies 24 different classes in addition to the satellite image class;however, it is also easy to distinguish the characteristics of those other classes, such as the face of human, basketball, cat, etc., because they have some distinctive features that make them easy to differentiate. The main problem with satellite pictures is that they might include a variety of various characteristics, which makes satellite image categorization challenging. The fact that safelight photographs typically include noise corruption is another problem. The SVM Classifier is used to estimate the noise patterns of the wireless picture, and the SVM technique for signal classification is used to eliminate the predicted noise patterns. This study initially segments the input test picture to be categorized, then employs the suggested EL-BP approach to identify the local binary patterns. Because the patterns of most of the satellite image and many other classes of images cannot be approximated using LBP alone, the Extended LBP is required. SVM identifies the test image's class after obtaining expanded features. The EL-BP SVM approach was utilized in this study, and the accuracy of satellite picture identification was 94%. The findings of the experiment using MATLAB 2018b are superior to prior work that has been done to categorize satellite photos. © 2022 ieee.

关键词： Local binary pattern

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HRM-CenterNet: A High-Resolution Real-time Fittings Detection Method

HRM-CenterNet: A High-Resolution Real-time Fittings Detectio...

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ieee international conference on systems, Man, and Cybernetics (SMC)

作者： Zhang, Ke Zhao, Kai Guo, Xiwang Feng, Xiaohan Tang, Ying North China Elect Power Univ Dept Elect & Commun Engn Baoding 071003 Hebei Peoples R China North China Elect Power Univ Hebei Key Lab Power Internet Things Technol Baoding 071003 Hebei Peoples R China Liaoning Shihua Univ Comp & Commun Engn Coll Fushun 113001 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Rowan Univ Elect & Comp Engn Glassboro NJ USA

ISBN: (纸本)9781665442077

Most successful fittings detectors are anchor-based, which is challenging to meet the lightweight and real-time requirements of the edge computing system. We propose a high-resolution real-time network HRM-CenterNet. Firstly, the lightweight MobileNetV3 is used to extract multi-level features from images. Then, to improve the resolution of the feature maps and reduce the spatial semantic information loss during the image downsampling process, a high-resolution feature fusion network based on iterative aggregation is introduced. Finally, we conduct experiments on the PASCAL VOC dataset and fittings dataset. The results show that HRM-CenterNet improves accuracy as well as robustness, and meets the performance requirements of real-time edge detection.

关键词： Iterative methods

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Towards 3D-Denser Ultrasound image Simulation from 2D CT-Scan for Ultrasound-Guided Percutaneous Nephrolithotomy Training

Towards 3D-Denser Ultrasound Image Simulation from 2D CT-Sca...

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ieee international conference on systems, Man and Cybernetics

作者： Sathiyamoorthy Selladurai Ben Sainsbury James Watterson Rebecca Hibbert Anila Satheesh B Arun K. Thittai Carlos Rossa Department of Systems and Computer Engineering Carleton University Ottawa Ontario Canada Marion Surgical Seguin Ontario Canada Faculty of Medicine Division of Urology University of Ottawa Ottawa ON Canada Department of Radiology Mayo Clinic Rochester MN USA Indian Inst. of Technology Madras Chennai India

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Virtual reality (VR) simulation can improve the outcomes of percutaneous nephrolithotomy (PNCL) - a surgery to extract kidney stones using ultrasound (US) or fluoroscopy image guidance. These simulators almost exclusively employ fluoroscopy, and no commercial VR simulator is available for US-guided PNCL (usPCNL). In this paper, we proposed the first step towards developing an usPCNL simulator that integrates a volumetric US model of the patient's anatomy derived from parallel 2D computed tomography (CT) scans. A critical challenge in US image generation from CT scans is that the limited spatial resolution of CT slices may lead to inaccuracies in the simulated US images. The proposed algorithm interpolates successive CT images to create an augmented dataset with increased spatial resolution. Each CT slice is then converted into a US image based on principles of linear acoustics and spatial impulse response. These images are then combined to form two different volumetric US images, one derived from the original sparse CT scans, and one created with the augmented data. From these volumetric US images, new images can be formed along arbitrary imaging planes not captured in the original CT data. The obtained simulated images are compared with their corresponding real US images acquired experimentally, and further evaluated quantitatively using normalized root mean square error (NRMSE) and dice similarity coefficient (DSC). The results reveal an NRMSE of $\mathbf{0.235}\pm \mathbf{0.051}$ and a DSC of $\mathbf{0.9139}\pm \mathbf{0.062}$ , showcasing a close resemblance between simulated and actual ultrasound images. Additionally, we show that denser CT scan data leads to a 25% improvement in image quality based on peak signal-to-noise ratio compared to the original dataset. This initial work is laying the foundation for the development of the usPCNL simulator, which could potentially have significant benefits for training and enhancing skills in this medical procedure.

关键词： Training Solid modeling Ultrasonic imaging PSNR image synthesis Computed tomography Surgery Virtual reality Spatial resolution Root mean square

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ieee international conference on Acoustics, Speech and signal Processing (ICASSP)

作者： Ma, Zhe Liu, Fenghao Dong, Jianfeng Qu, Xiaoye He, Yuan Ji, Shouling Zhejiang Univ Hangzhou Peoples R China Zhejiang Gongshang Univ Hangzhou Peoples R China Alibaba Zhejiang Univ Joint Res Inst Frontier Tec Hangzhou Peoples R China Huazhong Univ Sci & Technol Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781728176055

This paper aims for the language-based product image retrieval task. The majority of previous works have made significant progress by designing network structure, similarity measurement, and loss function. However, they typically perform vision-text matching at certain granularity regardless of the intrinsic multiple granularities of images. In this paper, we focus on the cross-modal similarity measurement, and propose a novel Hierarchical Similarity Learning (HSL) network. HSL first learns multi-level representations of input data by stacked encoders, and object-granularity similarity and image-granularity similarity are computed at each level. All the similarities are combined as the final hierarchical cross-modal similarity. Experiments on a large-scale product retrieval dataset demonstrate the effectiveness of our proposed method. Code and data are available at https://***/liufh1/hsl.

关键词： Product retrieval Hierarchical similarity Multi-level representation Cross-modal retrieval

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