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Self-Transriber: Few-Shot Lyrics Transcription With Self-Training 48

Self-Transriber: Few-Shot Lyrics Transcription With Self-Tra...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Gao, Xiaoxue Yue, Xianghu Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The current lyrics transcription approaches heavily rely on supervised learning with labeled data, but such data are scarce and manual labeling of singing is expensive. How to benefit from unlabeled data and alleviate limited data problem have not been explored for lyrics transcription. We propose the first semi-supervised lyrics transcription paradigm, Self-Transcriber, by leveraging on unlabeled data using selftraining with noisy student augmentation. We attempt to demonstrate the possibility of lyrics transcription with a few amount of labeled data. Self-Transcriber generates pseudo labels of the unlabeled singing using teacher model, and augments pseudo-labels to the labeled data for student model update with both self-training and supervised training losses. This work closes the gap between supervised and semi- supervised learning as well as opens doors for few-shot learning of lyrics transcription. Our experiments show that our approach using only 12.7 hours of labeled data achieves competitive performance compared with the supervised approaches trained on 149.1 hours of labeled data for lyrics transcription. © 2023 ieee.

关键词： Lyrics transcription music information retrieval singing processing and self-training

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Investigation on Deep Learning and Machine Learning Approaches for Antenna Design Based on Radar signal processing 4

Investigation on Deep Learning and Machine Learning Approach...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Govindasamy, Santhakumar Bhushan, Kavi Nijhawan, Ginni Kumar, B Santhosh Chandrashekar, Rakesh Issa, Ali Ashoor Sri Krishna College of Technology and Tamilnadu Electronics and Communication Engineering India Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

This paper gives a complete review of the use of Deep Learning (DL) and Machine Learning (ML) approaches in the area of antenna design for radar data processing. It analyzes the potential of DL and ML to overcome the constraints of conventional antenna design methodologies, especially in the face of complicated environmental variables and the necessity for interference reduction. By employing sophisticated computational techniques, the research reveals how these AI-based approaches may considerably boost the design and performance optimization of radar antennas. The integration of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning into the design process offers promise for constructing adaptable and efficient radar systems. Empirical findings from the research illustrate the durability of ML models, notably Support Vector Machines (SVMs), in forecasting antenna performance, stressing its resilience even in high-noise environments. This investigation's results are crucial for the progress of intelligent radar systems and lay the way for future improvements in the industry. © 2024 ieee.

关键词： Support vector machines

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Research on multi-step short-term power load forecasting based on optimized VMD signal processing and PSO-SVR 7

Research on multi-step short-term power load forecasting bas...

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7th ieee international conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Tan, Xuebiao Wang, Jing Ding, Dan Kang, Lun Fu, Wentian Wang, Haiwen Ran, Yunzheng Zhong, Jianwei Enshi Power Supply Company State Grid Hubei Electric Power Co Ltd Enshi China School of Intelligent Science and Engineering Hubei Minzu University Enshi China

ISBN: (纸本)9798350350760

Accurate load forecasting is beneficial for the rational scheduling of power generation energy, meeting people's daily electricity needs, and facilitating the steady development of the Chinese economy. It is difficult to predict short-term load demand using historical load data, mainly due to the difficulties caused by its nonlinear characteristics. Therefore, this article proposes a VMD-PSO-SVR short-term forecasting model for the power load of an industrial park. This model uses Variational Mode Decomposition (VMD) to split the original historical load data into several nonlinear subsequences. Then, selective reconstruction is performed based on the degree of correlation between the subsequences and the original sequence to form several new subsequences. Next, the easily predictable subsequences are input into a support vector regression prediction model optimized by particle swarm optimization (PSO) for prediction. Finally, the predicted results of each component are reconstructed to obtain the final prediction result. A case study in an industrial park concludes that the average absolute percentage error (MAPE%) and goodness of fit test (R2%) evaluation indicators of the VMD-PSO-SVR prediction model are 0.6415% and 99.7476%, respectively. Compared with other models, it can more accurately predict future short-term load demand. ©2024 ieee.

关键词： Variational mode decomposition

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Robust Wake Word Spotting With Frame-Level Cross-Modal Attention Based Audio-Visual Conformer

Robust Wake Word Spotting With Frame-Level Cross-Modal Atten...

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

作者： Haoxu Wang Ming Cheng Qiang Fu Ming Li Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China

In recent years, neural network-based Wake Word Spotting achieves good performance on clean audio samples but struggles in noisy environments. Audio-Visual Wake Word Spotting (AVWWS) receives lots of attention because visual lip movement information is not affected by complex acoustic scenes. Previous works usually use simple addition or concatenation for multi-modal fusion. The inter-modal correlation remains relatively under-explored. In this paper, we propose a novel module called Frame-Level Cross-Modal Attention (FLCMA) to improve the performance of AVWWS systems. This module can help model multi-modal information at the frame-level through synchronous lip movements and speech signals. We train the end-to-end FLCMA based Audio-Visual Conformer and further improve the performance by fine-tuning pre-trained uni-modal models for the AVWWS task. The proposed system achieves a new state-of-the-art result (4.57% WWS score) on the far-field MISP dataset.

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Extreme-learning-machine-based architecture to assess the reliability of the integrated energy systems 8

Extreme-learning-machine-based architecture to assess the re...

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8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Huo, Xianxu Wang, Qingbiao Zhao, Chenyang Ding, Yi Zhang, Bingchen Electric Power Research Institute of State Grid Tianjin Electric Power Company Tianjin300384 China State Grid Tianjin Electric Power Company Baodi Power Supply Branch Tianjin301800 China State Grid Tianjin Electric Power Company Tianjin300010 China Tianjin University Key Laboratory of Smart Grid of Ministry of Education Tianjin300072 China

ISBN: (纸本)9798350302455

The global energy crisis has contributed to the development of integrated energy systems (IES). IES enables the coupling, conversion, and utilisation of multiple energy, such as electricity, heat and cooling. As renewable energy penetration increases, the precariousness of renewable energy in IES causes risks to the reliability of energy supply. Traditional optimal energy flow (OEF) models become complex and computationally intensive. To solve this problem, a machine-learning-based method is proposed instead of the traditional OEF model. The method obtains the model optimal coding parameters by extreme learning machine (ELM) model, establishes the nonlinear mapping relationship between the system operation state and the optimal load reduction, and realizes the high accuracy and fast solution of the reliability indices. In this article, simulation experiments are conducted on the integrated community energy system (ICES) to test the advantage and practicality of the approach in this paper. Compared with the model-driven approach, the method in this paper greatly improves the efficiency of reliability assessment and has an accuracy rate of over 95%, which is better than random forest, back propagation neural network and autoencoder. © 2023 ieee.

关键词： Deep learning

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Hardware Implementation of an efficient FIR filter for ECG signal Denoising Application 29

Hardware Implementation of an efficient FIR filter for ECG S...

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29th ieee international conference on Electronics, Circuits and systems (ieee ICECS)

作者： Ajaydas, U. R. Reddy, B. Naresh Kumar James, Alex Digital Univ Kerala Sch Elect Syst & Automat Veiloor India

ISBN: (数字)9781665488235

ISBN: (纸本)9781665488235

In this paper, an FIR filter using Vedic Multiplier and Carry Look Ahead adder (VMCLA) is proposed for Electrocardiogram (ECG) denoising. Vedic Multiplier based on Urdhva Tiryaghbhyam technique is designed because of its faster and better computational ability. Further, the delay in addition of partial products is also reduced by using Carry Look Ahead (CLA) Adder. ECG signal processing and validation of performance parameters are done in MATLAB. The proposed FIR filter design algorithm is synthesized and simulated using Vivado design suite 2021.2 and implemented on Kintex-7 FPGA board. The results revealed a high performance and area efficient compared to a well-known prior art filters.

关键词： Electrocardiogram (ECG) Vedic Multiplier (VM) Urdhva Tiryagbhyam Sutra Carry Look Ahead (CLA) Adder Performance

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Smart Sports Video Tracking and Safety Solutions Integrating GPS and Satellite Communications 4

Smart Sports Video Tracking and Safety Solutions Integrating...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ying, Sui Tianjin Vocational College of Communications Tianjin300110 China

ISBN: (纸本)9798331523923

As the system of intelligent sensing and video surveillance continues to improve, its application in different fields has also emerged. In this study, the smart sports video tracking and safety solutions integrating the GPS and satellite communications is discussed. The core goal of this research is to address the needs of athlete tracking and safety in complex environments. The designed system consists of three core modules, namely, efficient video tracking module, high-precision GPS tracking module and stable satellite communication module. First, the proposed video tracking module uses a Laplace operator-based deblurring algorithm for video image processing. Subsequently, the human target in the moving video is accurately tracked through the two-dimensional flow field distribution. As a global optimization, the edge similarity feature segmentation is used to achieve dynamic marker tracking. The designed GPS tracking module uses the single difference observation method to improve positioning accuracy. This module combines the direct rounding fuzzy solution to effectively solve the coordinate conversion and measurement error problems. Finally, the satellite communication module used works through the coordination of the main control machine, main controller and signal switching module. This architecture realizes efficient data transmission and processing. Through experimental verification, the effectiveness of this tracking and assurance framework can be explored. © 2025 ieee.

关键词： Satellite communication systems

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Intrusion Detection and Prevention at the End-User Level in Smart Grid Cyber-Physical systems 2

Intrusion Detection and Prevention at the End-User Level in ...

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2nd ieee international conference on Innovations in High-Speed Communication and signal processing, IHCSP 2024

作者： Balaji, N.V. Mittal, Sachin Senthilkumar, K.K. Sood, Gourav Manjunatha, R. Wankhede, D.S. Karpagam Academy of Higher Education Department of Computer Science Coimbatore 641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura 140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai 127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Jain (Deemed to be University) School of Sciences Karnataka Bangalore India Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350368949

In order to keep the electric power system running smoothly and reliably, it is constantly monitored and controlled by an intelligent cyber layer that consists of data processing algorithms and a wide network of sensors. Cyberattacks on this data processing and collecting system might halt operations and have catastrophic physical consequences like system breakdowns. A common kind of attack is the FDIA. To avoid detection by the state estimator's inherent anomaly detection capabilities, an attacker tries to trick the grid's underlying control system into producing disturbances. On top of that, there are major issues with customer-end security. For instance, criminals might intercept, alter, or replay wireless transmissions of power consumption signals sent by consumers to their utility provider. Consequently, smart grid security and privacy are major apprehensions. In order to prevent cyberattacks on the vital infrastructures of the electrical system, this thesis helps. New detection systems for the generating and transmission side, as well as the end-user customer side infrastructure, are the primary focus of the study. To protect the generating and transmission sides from cyber-attack, we provide a system based on intelligent sensor weights and an optimization strategy based on sophisticated grey wolves. In addition, to safeguard end-user customer data from cyberattacks, we provide a gaussian mixture model-based privacy protection strategy and an artificial neural structure-based intelligent loop detection method. © 2024 ieee.

关键词： AMI CI Cyber-security DAP DPC and Smart-Grid EPPS FDIA GMM

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An Analysis of the Brake Performance of Radar-based Adaptive Cruise Control During Ramp Merging on Simulation Software 7

An Analysis of the Brake Performance of Radar-based Adaptive...

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7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Zhao, Zhanle Wei, Chuheng University of Warwick Warwick Manufacturing Group Coventry United Kingdom

ISBN: (纸本)9781665478571

Adaptive Cruise Control System (ACC) is an essential ADAS system for the autonomous vehicle for preventing all kinds of collision in the complex traffic and road situations. Ramp merging has been considered as one of the special traffic situations that can be used for testing the performance of ACC and sensor fusion requirements. To realise the project outcomes, the parametres of Long-Range Radar and Short-Range Radar has been implemented onto the IPG CarMaker with several scenarios of ramp merging been generated for simulating the ACC performance under different sensors and sensor fusion strategies. According to the simulation result, the stability of ACC performance under ramp merging needs to be permitted through adding sensor fusion between Long and Short Range Radar in order to prevent all kinds of collision between different dynamic models of ego and front target vehicle. © 2022 ieee.

关键词： Adaptive control systems

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Performance and Latency Analysis of EEG signal for Control System of Robotic Arm Movements Based on Sliding Mode Controller 22

Performance and Latency Analysis of EEG Signal for Control S...

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22nd ieee international Multi-conference on systems, signals and Devices, SSD 2025

作者： Alrawi, Ali Amer Ahmed Al-Ani, Abdullah Mashhadany, Yousif Al Algburi, Sameer University of Anbar Biomedical Engineering Research Center (BERC) Anbar Iraq Engineering College University of Anbar Electrical Engineering Department Anbar Iraq College of Engineering Al-Kitab University Kurkuk Iraq

ISBN: (纸本)9798331542726

The integration of brain-computer interfaces (BCIs) in robotics presents a novel approach to enable intuitive control of robotic systems. This paper proposes a real-time control methodology for a two-link robot arm utilizing synthetic electroencephalography (EEG) signals combined with a sliding mode control (SMC) strategy. The objective is to achieve precise manipulation of the robotic arm in response to the classified EEG signals, simulating brain activities that indicate movement intentions. We first generate a synthetic EEG dataset to represent brain signals indicative of left and right arm movements. This dataset serves as the input for a classification algorithm that determines the desired motion of the robotic arm. The proposed control framework leverages SMC, known for its robustness against system uncertainties and external disturbances, ensuring stable performance even in dynamic environments. The dynamics of the two-link robot arm are modeled, incorporating the non-linarites associated with joint movements. The control law is designed to achieve the desired angles through a sliding surface that adapts to the specified EEG signals. Simulation results demonstrate the efficacy of the control approach, showing rapid convergence of the robot arm to the target positions based on the EEG-derived commands. Moreover, the paper presents comprehensive performance metrics, including the trajectory tracking accuracy and response time of the robotic arm. Visualizations illustrate the arm's motion during the execution of left and right movements, providing a clear understanding of the system's dynamics in action. This research highlights the potential of EEG-driven control in enhancing human-robot interaction, paving the way for future developments in assistive technologies and rehabilitation robotics. The findings indicate a promising direction for integrating BCIs in robotic systems, contributing to advancements in intelligent automation. © 2025 ieee.

关键词： Control systems EEG signal Robot Arm Robustness Sliding Mode Control

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