2,845 research outputs found
Automotive three-microphone voice activity detector and noise-canceller
This paper addresses issues in improving hands-free speech recognition performance in car
environments. A three-microphone array has been used to form a beamformer with leastmean
squares (LMS) to improve Signal to Noise Ratio (SNR). A three-microphone array
has been paralleled to a Voice Activity Detection (VAD). The VAD uses time-delay
estimation together with magnitude-squared coherence (MSC)
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Intelligent Learning Algorithms for Active Vibration Control
YesThis correspondence presents an investigation into the
comparative performance of an active vibration control (AVC) system
using a number of intelligent learning algorithms. Recursive least square
(RLS), evolutionary genetic algorithms (GAs), general regression neural
network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS)
algorithms are proposed to develop the mechanisms of an AVC system.
The controller is designed on the basis of optimal vibration suppression
using a plant model. A simulation platform of a flexible beam system
in transverse vibration using a finite difference method is considered to
demonstrate the capabilities of the AVC system using RLS, GAs, GRNN,
and ANFIS. The simulation model of the AVC system is implemented,
tested, and its performance is assessed for the system identification models
using the proposed algorithms. Finally, a comparative performance of the
algorithms in implementing the model of the AVC system is presented and
discussed through a set of experiments
Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things
The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62
Active Noise Control with Sampled-Data Filtered-x Adaptive Algorithm
Analysis and design of filtered-x adaptive algorithms are conventionally done
by assuming that the transfer function in the secondary path is a discrete-time
system. However, in real systems such as active noise control, the secondary
path is a continuous-time system. Therefore, such a system should be analyzed
and designed as a hybrid system including discrete- and continuous- time
systems and AD/DA devices. In this article, we propose a hybrid design taking
account of continuous-time behavior of the secondary path via lifting
(continuous-time polyphase decomposition) technique in sampled-data control
theory
A Novel Method for Acoustic Noise Cancellation
Over the last several years Acoustic Noise Cancellation (ANC) has been an active area of research and various adaptive techniques have been implemented to achieve a
better online acoustic noise cancellation scheme. Here we introduce the various adaptive techniques applied to ANC viz. the LMS algorithm, the Filtered-X LMS algorithm, the Filtered-S LMS algorithm and the Volterra Filtered-X LMS algorithm and try to understand their performance through various simulations. We then take up the problem of cancellation of external acoustic feedback in hearing aid. We provide three different models to achieve the feedback cancellation. These are - the adaptive FIR Filtered-X LMS, the adaptive IIR LMS and the adaptive IIR PSO models for
external feedback cancellation. Finally we come up with a comparative study of the performance of these models based on the normalized mean square error minimization provided by each of these feedback cancellation schemes
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