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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

共 3487 条记录，以下是21-30 订阅

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Accelerating Codec-based Speech Synthesis with Multi-Token Prediction and Speculative Decoding

Accelerating Codec-based Speech Synthesis with Multi-Token P...

引用

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

作者： Tan Dat Nguyen Ji-Hoon Kim Jeongsoo Choi Shukjae Choi Jinseok Park Younglo Lee Joon Son Chung Korea Advanced Institute of Science and Technology South Korea 42dot Inc South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of this paper is to accelerate codec-based speech synthesis systems with minimum sacrifice to speech quality. We propose an enhanced inference method that allows for flexible trade-offs between speed and quality during inference without requiring additional training. Our core idea is to predict multiple tokens per inference step of the AR module using multiple prediction heads, resulting in a linear reduction in synthesis time as the number of heads increases. Furthermore, we introduce a novel speculative decoding technique that utilises a Viterbi-based algorithm to select the optimal sequence of generated tokens at each decoding step. In our experiments, we demonstrate that the time required to predict each token is reduced by a factor of 4 to 5 compared to baseline models, with minimal quality trade-off or even improvement in terms of speech intelligibility. Audio samples are available at: ***/projects/mtp.

关键词： Training Speech coding signal processing algorithms signal processing Predictive models Prediction algorithms Inference algorithms Acoustics Decoding Speech synthesis

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A Two-timescale Primal-dual Algorithm for Decentralized Optimization with Compression

A Two-timescale Primal-dual Algorithm for Decentralized Opti...

引用

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

作者： Haoming Liu Chung-Yiu Yau Hoi-To Wai Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper proposes a two-timescale compressed primal-dual (TiCoPD) algorithm for decentralized optimization with improved communication efficiency over prior works on primal-dual decentralized optimization. The algorithm is built upon the primal-dual optimization framework and utilizes a majorization-minimization procedure. The latter naturally suggests the agents to share a compressed difference term during the iteration. Furthermore, the TiCoPD algorithm incorporates a fast timescale mirror sequence for agent consensus on nonlinearly compressed terms, together with a slow timescale primal-dual recursion for optimizing the objective function. We show that the TiCoPD algorithm converges with a constant step size. It also finds an $\mathcal{O}(1/T)$ stationary solution after T iterations. Numerical experiments on decentralized training of a neural network validate the efficacy of TiCoPD algorithm.

关键词： Training Machine learning algorithms Neural networks signal processing algorithms Machine learning signal processing Linear programming Mirrors Speech processing Optimization

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Multimodal Emotion Recognition with Target Speaker-Based Facial Embeddings

Multimodal Emotion Recognition with Target Speaker-Based Fac...

引用

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

作者： Serin Heo Jehyun Kyung Joon-Hyuk Chang Department of Artificial Intelligence Hanyang University Seoul Republic of Korea Department of Electronic Engineering Hanyang University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Effectively recognizing emotions requires sophisticated approaches for interpreting diverse modalities, particularly in real-world scenarios where multiple data sources, such as speech, text, and visual cues, are often noisy and incomplete. This study proposes an advanced multimodal emotion recognition system that integrates these three modalities by adding the speaker detection and extraction algorithm within visual data. The pre-trained Q-Former used in the proposed system then captures and interprets visual signals supported with designated prompts, resulting in facial-related features that significantly improve emotion recognition performance. We then utilize a cross-modal transformer to unify the visual, speech, and text embeddings for accurate emotion classification. We achieved a 2.9% and 3.3% improvement in accuracy and F1 score, respectively, on the MELD dataset compared to the baseline.

关键词： Emotion recognition Visualization Accuracy Text recognition signal processing algorithms Speech recognition Transformers Classification algorithms Data mining Speech processing

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RADAR: Robust Anomaly Detection and Recognition for IoT Botnet Attacks Using Hyper-Dimensional Models

RADAR: Robust Anomaly Detection and Recognition for IoT Botn...

引用

International conference on signal processing and Communication (ICSC)

作者： Abdullah M Alqarni Faiz S Alshehri Majed S Alharbi Nitin Talreja Sajal Bhatia School of Computer Science and Engineering Sacred Heart University Fairfield USA Principal Data Engineer Optum Services Inc. (United Health Group) Hartford USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

As our world continues to embrace digital technology, cyber threats have become increasingly prevalent, especially botnet attacks. Ensuring cybersecurity is a significant concern, and to address this, we need Intrusion Detection Systems (IDS) that can automatically adapt to new threats using advanced machine learning (ML) and deep learning (DL) techniques. Our project aims to develop an advanced anomaly detection and recognition system for IoT botnet attacks using hyper-dimensional models namely Random Forest, Decision Tree, Gradient Boosting, Multi-layer Perceptron (MLP), and K-Nearest Neighbor (KNN) and makes uses of hyperparameter tuning for performance optimization. By integrating an ensemble voting classifier that combines these five algorithms using a soft-voting mechanism, our results indicate an improved detection accuracy and adaptability. The proposed ensemble-based model is tested on a publicly available dataset which offers comprehensive coverage of botnet attack patterns and outperforms all individual classifiers, achieving an accuracy of 99.88% and an ROC AUC score of 0.999. The proposed approach highlights the advantages of combining hyper-dimensional classification models in effectively detecting IoT botnet attacks.

关键词： Adaptation models Accuracy Botnet Intrusion detection signal processing algorithms Nearest neighbor methods Radar signal processing Anomaly detection Tuning Random forests

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Research on advanced Control Strategies for Building Energy Management Systems to Optimize Energy Consumption in Commercial Buildings by Using DRL-PC Algorithm

Research on Advanced Control Strategies for Building Energy ...

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International conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Nuzat Nuary Alam Rethwan Faiz Abu Hena Md Shatil Md.Araf Ohid MD. Abdullah Al Akib MD. Yeasir Arafat Khan Dept. of EEE American Int. Univ. Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

This study investigates the application of Deep Rein-forcement Learning combined with Predictive Control (DRL-PC) algorithms in Building Energy Management Systems (BEMS) for commercial buildings in Dhaka, Bangladesh. The research compares the performance of a DRL-PC algorithm against a traditional rule-based control system in a 10-story office building over a 12-month period. The DRL-PC algorithm demonstrated significant improvements, achieving a 21 % reduction in annual energy consumption, a 25 % increase in occupant comfort sat-isfaction, and a 28% reduction in peak energy demand. The system showed remarkable adaptability to Dhaka's challenging climate, maintaining optimal indoor conditions 92 % of the time. With a payback period of 2.7 years, the proposed system offers a promising solution for enhancing energy efficiency and occupant comfort in commercial buildings, particularly in regions with complex climatic conditions.

关键词： Energy consumption Renewable energy sources System performance signal processing algorithms signal processing Prediction algorithms Smart grids Robots Building management systems Predictive control

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Myoelectric Controlled Lower Limb Prosthesis With Active Ankle-Foot Joint

Myoelectric Controlled Lower Limb Prosthesis With Active Ank...

引用

International conference on Biosignals, Images and Instrumentation (ICBSII)

作者： M.Neela Harish S. Sathish K Harini Sri Kamatchi S. Prasanthi K Anushiya Department of Biomedical engineering Easwari Engineering College Chennai India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

The rising prevalence of lower limb amputations necessitates advanced prostheses to restore mobility and independence. This project focuses on designing a myoelectric-controlled lower limb prosthesis with an active ankle-foot joint. EMG signals from the amputee’s residual limb are processed to extract patterns, enabling real-time prosthetic control based on user intent. Powered by stepper motors, the active joint ensures smooth, precise movements.A microcontroller handles signal processing and motor control, enhancing functionality and adaptability. By integrating advanced signal processing with precise motor control, this system bridges the gap between user intent and prosthetic action, improving mobility & user-specific control.

关键词： Pressure sensors Limbs Motor drives signal processing algorithms Sensor fusion Electromyography Real-time systems Usability Prosthetics Predictive control

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Deep Learning Approaches for Enhancing Cardiovascular Risk Assessment

Deep Learning Approaches for Enhancing Cardiovascular Risk A...

引用

International conference on Inventive Computation Technologies (ICICT)

作者： Priyatharsini C Sithdesh C S Sivasooriyan P Annamalai S Gobiraj M Computer Science And Engineering Mahendra Engineering College Namakkal

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

advanced diagnostic technologies are necessary for early diagnosis and treatment of cardiovascular disease (CVD), a rising global health concern. By combining raw ECG signals with patient demographic information, the suggested approach enhances the prediction of heart illness. To improve the system's anticipated accuracy, this method incorporates signal processing, feature extraction, and selection processes. Thus, we create an application that uses 12 lead-ECG signal images from PTB-XL dataset along with additional parameters like age, gender, height, and weight to forecast the vulnerability of a heart condition. This system method derives valuable properties from ECG data using a new Fast Fourier Transform (FFT) feature extraction algorithm. Furthermore, the random forest classification technique is used to select highly discriminative characteristics for prediction. Acquired features are input into a Multi-Layer Perceptron (MLP) model, which is trained by employing an extensive dataset of patient demographics and ECG signals.

关键词： Heart Fast Fourier transforms signal processing algorithms Electrocardiography signal processing Multilayer perceptrons Feature extraction Prediction algorithms Risk management Random forests

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A New Method to Detect DMR Faults During Balanced Condition for a VSC Bipole Onshore-Offshore HVDC Network

A New Method to Detect DMR Faults During Balanced Condition ...

引用

Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International conference on

作者： Sauvik Biswas Saikat Majumdar Kiran Kumar Math Sunil Joshi Omar Jasim GE Vernova Noida India GE Vernova Stafford UK

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

The bipole High-Voltage Direct Current (HVDC) systems can transmit high power rating when compared with monopole HVDC link. The flexibility and reliability that is provided by the bipole configuration make it an attractive solution for power transmission from offshore wind farms to onshore grid. These bipole HVDCs have dedicated metallic return (DMR) as a return path and ground references. The detection of fault in the DMR cable of a bipole HVDC is a challenging task. Traditional over-current and differential protection fail to detect the faults in the network. In this paper, the moving window-based Pearson correlation coefficient (MWPCC), variational mode decomposition (VMD) and bagging ensemble classifier (BEC) based two fault detection algorithms are proposed. The first two advanced signal processing methods are utilized to extract data for the classifier. The effectiveness of the proposed detection technique is demonstrated by applying different fault scenarios. The time domain simulations are performed using a full-scale industrial model, and the fault detection algorithms have achieved very high accuracy to detect the fault.

关键词： Correlation coefficient HVDC transmission Fault detection signal processing algorithms Wind farms signal processing Classification algorithms Time-domain analysis Sustainable development Bagging

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Arc detection algorithm using Photo Multiplier Tubes

Arc detection algorithm using Photo Multiplier Tubes

引用

IEEE/SEMI conference and Workshop on advanced Semiconductor Manufacturing

作者： Sunjung Kim Minjun Kwak Hyeonjoong Kim Sarah Kim Changyul Kim Myunggeun Song Jungmin Lee Jaeseok Park Younghoon Kim Jaeyong Park Autonomous Fab Team Samsung Electronics Hwaseong Si South Korea AI Center Tata Electronics Santa Clara USA

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

This paper presents an advanced algorithmic framework for enhanced detection of high-voltage discharge events (arcs) through photomultiplier tube (PMT) signal analysis, demonstrating substantial improvements in critical arc event classification and yield preservation. Arc detection accuracy is paramount for process control and equipment integrity in plasma-based semiconductor manufacturing. This optimization is especially critical as feature densities increase and critical dimensions decrease in advanced node semiconductor fabrication.

关键词： Photomultipliers Accuracy Noise reduction signal processing algorithms Semiconductor device manufacture Data aggregation Market research Etching signal resolution Field programmable gate arrays

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Smart Parking and Automation Based System as Dynamic Approach

Smart Parking and Automation Based System as Dynamic Approac...

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International conference on signal processing and Communication (ICSC)

作者： K. Sherin A.R. Darshika Kelin Hains Immanuel Vaiz J St. Joseph's Institute of Technology Chennai Tamil Nadu India Karunya Institute of Technology and Science Coimbatore Tamil Nadu India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Efficient management of growing urban parking demand would require smart parking systems. A dynamic approach will be presented: using IoT-enabled algorithms, predictive analytics, and processing real-time data to optimize space utilization in the parking lot and provide convenience for users while allowing for the maximum possible operational revenue. The system achieves this by utilizing IoT sensors such as ultrasonic, infrared, and magnetic field sensors to accurately detect the occupancy of parking spaces. algorithms for sensor data aggregation preprocess and fuse the information so that it can be relied upon and, as a result, allows for real-time monitoring. Dynamic reservation and pricing algorithms are built into the system that balances parking fees according to real-time demand, applying techniques like surge pricing, time-based adjustments, and predictive forecasting to balance occupancy and maximize revenue. Predictive analytics further supports demand forecasting by using historical data, user behavior, and environmental factors, allowing for advanced resource allocation and proactive price adjustment during peak hours. Further enhancements are added to the system in the form of edge and corner detection techniques for better vehicle identification and reservation management. The dynamic capabilities of the proposed system enhance the efficiency of operation, improve the user experience by allowing real-time spot allocation and reservations, and reduce parking congestion.

关键词： Heuristic algorithms Magnetic sensors signal processing algorithms Pricing Automated parking Real-time systems Sensor systems Resource management Vehicle dynamics Predictive analytics

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