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An Interference Cancellation Method Using Fixed Beamformer and Adaptive Filter in Car Environment 2020

An Interference Cancellation Method Using Fixed Beamformer a...

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3rd international conference on signal processing and machine learning, spml 2020

作者： Liu, Xi Yang, Xiaoxia Digital Signal Processing Department Volkswagen-Mobvoi Beijing China

ISBN: (纸本)9781450375733

In-car speech interactive system usually requires to selectively be controlled by one or more person in the vehicle. Beamforming is often introduced to deal with this case by using microphone array. However, most of the beamforming methods like fixed beamformer, Minimum Variance Distortionless Response (MVDR) and Generalized Sidelobe Canceller (GSC) show limited performance when two or more passengers speak simultaneously. Speech recognition pass rate will be severely degraded by the residual interference. And in car environment, speaker's location is previously known and relatively fixed, so an optimized interference cancellation method is proposed to separate desired signal from interferences. The proposed method consists of two parts, beamforming by using multiple fixed beamformer and optimized joint adaptive filter. The proposed beamforming method can provide better reference signal with less desired signal component compared with conventional beamforming method. Thus, by utilizing a powerful adaptive filter, residual noise and interference can be further suppressed with acceptable speech distortion. Simulation and real car experimental results show significant improvement on signal to noise plus interference ratio (SINR) and character error rate (CER). © 2020 ACM.

关键词： Beamforming

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Real-time Audio Recognition for Hearing Impaired

Real-time Audio Recognition for Hearing Impaired

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Muralidhar Peddi S H V Harsha Vardhan Giridhar Peddi Satwik Ponnam C Rani M Rajesh Kumar School of Electronics Engineering Vellore Institute of Technology Vellore India Electrical Engineering University of California Riverside Riverside USA Computer Science Arizona State University Tempe USA School of Electrical Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Audio recognition technology has seen significant advancements in recent years, offering promising solutions to improve communication and accessibility for deaf individuals. This paper explores the landscape of audio recognition technologies tailored specifically for the deaf community. It delves into the challenges faced by deaf individuals in everyday communication scenarios and how innovative audio recognition systems can mitigate these challenges. The abstract discusses various approaches, including speech-to-text systems, sound recognition algorithms, and real-time captioning tools, highlighting their effectiveness and limitations in facilitating seamless communication for deaf individuals. Furthermore, it examines the integration of machine learning and artificial intelligence techniques in enhancing the accuracy and adaptability of audio recognition systems for diverse environments and user preferences. Additionally, the abstract explores the potential socio-economic impacts of the widespread adoption of audio recognition technologies in empowering deaf individuals, fostering inclusion, and promoting equal opportunities in education, employment, and social interactions.

关键词： Training Accuracy signal processing algorithms Switches Real-time systems User experience Robustness

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Ontology learning using Hybrid machine learning Algorithms for Disaster Risk Management 2020

Ontology Learning using Hybrid Machine Learning Algorithms f...

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3rd international conference on signal processing and machine learning, spml 2020

作者： Contreras, Jennifer O. Ballera, Melvin A. Festijo, Enrique D. Future University Sudan Technological Institute of the Philippines Manila Philippines Technological Institute of the Philippines Manila Philippines

ISBN: (纸本)9781450375733

Disaster is inevitable but manageable thru careful planning, preparation and immediate response strategies. During typhoons, earthquakes and other calamities, agreement about language is vital to understand each other well to avoid high number of deaths, delay in access to basic needs and slow response time. However, some of the people involved in this domain find it hard to coordinate and respond to different emergency situations due to lack of familiarization and knowledge about the different terms or concepts. In disaster risk management, the consistency and reusability of the sharing of information is important to avoid possible risks. Due to this reason, an ontology is incorporated to aid in the disaster management process. The use of ontology enables quick retrieving and incorporating "consistent data"and information related to disaster management which plays an important for making decisions efficiently. This paper aims to implement and evaluate the accuracy of Support Vector machine (SVM) and Neural Network (NN) learning-based ontology for disaster risk management to enhance the classification of concepts (keywords) generated for the domain ontology. The experiment shows that the hybrid SVM and NN machine learning algorithm outperformed the accuracy of SVM and NN based on the precision, recall and F-Measure criterion. © 2020 ACM.

关键词： Support vector machines

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machine learning Algorithms for the Diagnosis of Cardiac Arrhythmia in IoT Environment 3rd

Machine Learning Algorithms for the Diagnosis of Cardiac Arr...

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3rd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2020

作者： Yadav, Samir Kadam, Vinod Jadhav, Shivajirao Dr. Babasaheb Ambedkar Technological University Lonere Raigad India

ISBN: (纸本)9789811604928

Cardiac arrhythmia is very harmful heart disease which can cause severe and even potentially deadly symptoms. Early diagnosis of an arrhythmia can save lives. In the modern health care environment, the use of Internet of Things (IoT) technologies brings the suitability for medical professionals and patients, because they are useful in various medical background. An arrhythmia diagnosis system based on IoT sensors helps and monitors automatic transmission and measurement of Electrocardiogram (ECG) signal data, analyzes this data, and alerts medical professional for an urgency. We are developing a new approach for monitoring and diagnosing cardiac arrhythmia using IoT environment. The concerned ECG signal data is generated by using the IoT sensors and standard online (UC Irvine repository) dataset for predicting severity of cardiac arrhythmia. Also, we used a new fuzzy logic-based neural network classifier algorithm for diagnosing cardiac arrhythmia. The experiments are carried out by various machine learning algorithms on standard UCI Repository dataset as well as on real patients data records collected from multiple Indian hospitals. The proposed machine learning algorithm offers the best accuracy than different machine learning approaches with minimum time complexity. Patients can check their heart condition at their home by using this system. The maintenance and development cost of this system is low due to it is a lightweight and small size system. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Enhancement of Degraded CCTV Footage for Forensic Analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

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3rd international conference on Innovative Computing and Communication, ICICC 2020

作者： Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K.N.B. Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

ISBN: (纸本)9789811551123

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

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Semantic Computing Enhancement of Industrial Augmented Reality Solutions with machine learning 3

Semantic Computing Enhancement of Industrial Augmented Reali...

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3rd international conference on Video, signal and Image processing, VSIP 2021

作者： Izquierdo-Domenech, Juan Linares-Pellicer, Jordi Orta-Lopez, Jorge Valencian Research Institute For Artificial Intelligence Polytechnic University Of Valencia Spain

ISBN: (纸本)9781450385886

Augmented reality (AR) systems in the industrial sector offer numerous advantages. From maintenance and repair processes to control and monitoring of complete manufacturing lines. All these applications focus on what we call the AR physical layer: determining the position and orientation of the user in front of the component to augment and superimpose synthetic elements aligned with the reality to facilitate the operator s task. In the present work, we propose the extension of this functionality using semantic computing. With techniques based on deep learning (DL), i.e., convolutional neural networks (CNNs), we add a semantic layer that interprets the environment and user actions from the physical layer of current AR systems. This semantic layer visually recognizes the status of both discrete and analog controls and obtains a set of values. These values, complemented by those from possible sensors, allow detecting anomalies or patterns of interest in decision-making using outliers and machine learning models for pattern detection. This information is also valuable in providing feedback to the AR system, giving the operators visually aligned information over the physical environment. © 2021 Association for Computing machinery. All rights reserved.

关键词： Augmented reality

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Bengali Accent Classification from Speech Using Different machine learning and Deep learning Techniques 3rd

Bengali Accent Classification from Speech Using Different Ma...

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3rd international conference on Computing and Communication, IC3 2020

作者： Badhon, S. M. Saiful Islam Rahaman, Habibur Rupon, Farea Rehnuma Abujar, Sheikh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9789811573934

The work starts with a question "Does human vocal folds produce different wavelength when they speak in different accent of same language?" Generally, when humans hear the language, they can easily classify the accent and region from the language. But the challenge was how we give this capability to the machine. By calculating discrete Fourier transform, Mel-spaced filter-bank and log filter-bank energies, we got Mel-frequency cepstral coefficients (MFCCs) of a voice which is the numeric representation of an analog signal. And then, we used different machine learning and deep learning algorithms to find the best possible accuracy. By detecting the region of speaker from voice, we can help security agencies and e-commerce marketing. Working with human natural language is a part of Natural Language processing (NLP) which is branch of artificial intelligence. For feature extraction, we used MFCCs, and for classification, we used linear regression, decision tree, gradient boosting, random forest and neural network. And we got max 86% accuracy on 9303 data. The data was collected from eight different regions (Dhaka, Khulna, Barisal, Rajshahi, Sylhet, Chittagong, Mymensingh and Noakhali) of Bangladesh. We follow a simple workflow for getting the ultimate result. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Filter banks

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3rd international conference on Innovative Computing and Communication, ICICC 2020

3rd International Conference on Innovative Computing and Com...

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3rd international conference on Innovative Computing and Communication, ICICC 2020

ISBN: (纸本)9789811551475

The proceedings contain 196 papers. The special focus in this conference is on Innovative Computing and Communication. The topics include: Tracking of soccer players using optical flow;selection of probabilistic data structures for spv wallet filtering;hybrid bf-pso algorithm for automatic voltage regulator system;malware classification using multi-layer perceptron model;protocol random forest model to enhance the effectiveness of intrusion detection identification;detecting user’s spreading influence using community structure and label propagation;bagging- and boosting-based latent fingerprint image classification and segmentation;selecting social robot by understanding human–robot interaction;flooding and forwarding based on efficient routing protocol;evaluating user influence in social networks using k-core;hardware-based parallelism scheme for image steganography speed up;predicting group size for software issues in an open-source software development environment;wireless cnc plotter for pcb using android application;epileptic seizure detection using machine learning techniques;analysis of minimum support price prediction for indian crops using machine learning and numerical methods;a robust methodology for creating large image datasets using a universal format;a comparative empirical evaluation of topic modeling techniques;an analysis of machine learning techniques for flood mitigation;link prediction in complex network: Nature inspired gravitation force approach;hridaya kalp: A prototype for second generation chronic heart disease detection and classification;depression anatomy using combinational deep neural network;two-stream mid-level fusion network for human activity detection;content classification using active learning approach;analysis of navic multipath signal sensitivity for soil moisture in presence of vegetation;transaction privacy preservations for blockchain technology;deeper into image classification.

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Optimizing Wavelength Division Multiplexing Free-Space Optical Systems: A Comparative Analysis of Predictive Modeling Techniques

Optimizing Wavelength Division Multiplexing Free-Space Optic...

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international conference on Advanced Electrical Engineering (ICAEE)

作者： Kebaili Rima Driz Samia Fassi Benattou Telecommunications and Digital Signal Processing Laboratory Faculty of Electrical Engineering University of Djillali Liabes Sidi Bel Abbes Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

In the ever-evolving landscape of telecommunications, Free-Space Optical (FSO) communication systems are increasingly positioned as promising candidates for next-generation networks. Their immunity to interference and high bandwidth perfectly align with the surging demand for high-speed data transmission. This research dives deep into the world of cutting edge predictive modeling techniques, which are essential for optimizing Wavelength Division Multiplexing FSO systems and addressing fading effects in atmospheric conditions. To rigorously assess the predictive power of various models, we utilize cutting edge OptiSystem simulations. Four machine learning (ML) models, including Fine Trees, linear Support Vector machines (SVMs), boosted trees, and linear regression models incorporating interaction terms, are evaluated to identify the most effective model for accurate performance prediction of the WDM- FSO system, specifically in terms of key metrics like Q-Factor, signal- to Noise Ratio (SNR) and Bit Error Rate (BER). A comprehensive approach is employed. Extensive data is generated through meticulous simulations of a wide range of atmospheric conditions. Further, leveraging rigorous evaluation techniques like Root Mean Squared Error (RMSE), R-squared, Mean Squared Error (MSE), and Mean Absolute Error (MAE), an in depth comparison of the four aforementioned algorithms is performed. This thorough analysis provides valuable insights into the strengths and weaknesses of each model, ultimately guiding the selection of the optimal model for next-generation WDM-FSO systems.

关键词： Q-factor Analytical models Free-space optical communication Atmospheric modeling Bit error rate Predictive models Wavelength division multiplexing Prediction algorithms Next generation networking signal to noise ratio

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Proceedings of the 2020 IEEE 3rd international conference on Data Stream Mining and processing, DSMP 2020

Proceedings of the 2020 IEEE 3rd International Conference on...

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3rd IEEE international conference on Data Stream Mining and processing, DSMP 2020

ISBN: (纸本)9781728132143

The proceedings contain 88 papers. The topics discussed include: information processing system for detection impurity in technical oil based on laser;hybrid machine learning system for solving fraud detection tasks;speech signal structuring method for biometric personality identification;kln: a deep neural network architecture for keypoint localization;training neural network over encrypted data;forecasting nonlinear nonstationary processes in machine learning task;the algorithmic classification trees;development of an adaptive module of the distance education system based on a hybrid neuro-fuzzy network;new approaches in the learning of complex-valued neural networks;trainable neural networks modeling for a forecasting of start-up product development;deep neo-fuzzy neural network and its accelerated learning;and two approaches to machine learning classification of time series based on recurrence plots.

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