Voice-Based Door Access Control System Using the Mel Frequency Cepstrum Coefficients and Gaussian Mixture Model

Akingbade, Kayode; Umanna, Okoko Mkpouto; Alimi, Isiaka A.

doi:10.11591/ijece.v4i5.6447

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Some event-based method using algorithm for real-time event detection such as change point detection [6], Generalized Likelihood Ratio (GLR) [7], the chi-square goodness-of-fit test (X2 GOF) [8], the Cumulative SUM (CUSUM) filtering [9], and cepstrum analysis [10]- [13]. Change point detection to detect abrupt changes in time series data and to identify the specific time instance when the change occurs.…”

Section: Related Workmentioning

confidence: 99%

Residential load event detection in NILM using robust cepstrum smoothing based method

Iksan¹,

Sembiring²,

Hariyanto³

et al. 2019

IJECE

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Event detection has an important role in detecting the switching of the state of the appliance in the residential environment. This paper proposed a robust smoothing method for cepstrum estimation using double smoothing i.e. the cepstrum smoothing and local linear regression method. The main problem is to reduce the variance of the home appliance peak signal. In the first step, the cepstrum smoothing method removed the unnecessary quefrency by applying a rectangular window to the cepstrum of the current signal. In the next step, the local regression smoothing weighted data points to be smoothed using robust least squares regression. The result of this research shows the variance of the peak signal is decreased and has a good performance with better accuracy. In noise enviromment, performance prediction quite good with values greater than 0.6 and relatively stable at values above 0.9 on SNR> 25 for single appliances. Furthermore, in multiple appliances, performance prediction quite good at SNR> 20 and begins to decrease in SNR <20 and SNR> 25.

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Section: Related Workmentioning

confidence: 99%

Residential load event detection in NILM using robust cepstrum smoothing based method

Iksan¹,

Sembiring²,

Hariyanto³

et al. 2019

IJECE

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“…The research on Automatic Speech Recognition (ASR) is still the most interest topic for its very important role in daily life such as in the number of works that have been conducted on SR (Speech Recognition) such as Smart Home [1][2][3][4], Artificial Intelligence such as human emotional classification based on speech recognition [5][6][7][8][9] and robotic application [10], in the field of Student Learning [11][12][13], in medical sector, SR is used to detect the stress level of a person [14]. Therefore, the optimization process in SR is still in the progress for obtaining the better accuracy such as with speech enhancement [15][16][17] or noise reduction [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

FPGA-based implementation of speech recognition for robocar control using MFCC

et al. 2019

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This research proposes a simulation of the logic series of speech recognition on the MFCC (Mel Frequency Spread Spectrum) based FPGA and Euclidean Distance to control the robotic car motion. The speech known would be used as a command to operate the robotic car. MFCC in this study was used in the feature extraction process, while Euclidean distance was applied in the feature classification process of each speech that later would be forwarded to the part of decision to give the control logic in robotic motor. The test that has been conducted showed that the logic series designed was precise here by measuring the Mel Frequency Warping and Power Cepstrum. With the achievement of logic design in this research proven with a comparison between the Matlab computation and Xilinx simulation, it enables to facilitate the researchers to continue its implementation to FPGA hardware.

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“…In the process of identifying spoken words can be displayed in written form or can be read by technological devices. Recognition of words through voice signals has been carried out by many previous studies, one of them is Makhraj recognition for Al-Quran recitation [1], voice-based door access control system [2], pronunciation of the alphabet in English [3], language recognition Bengali automatically [4], play songs automatically [5]. While other study identified speaker [6], recognized of a person through footstep sound [7], identified Qori reciter in Arabic [8], identified speakers with whispered speech audio flow [9].…”

Section: Introductionmentioning

confidence: 99%

Speaker and Speech Recognition Using Hierarchy Support Vector Machine and Backpropagation

Fadlilah¹,

Djamal²

2019

EECSI

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Voice signal processing has been proposed to improve effectiveness and facilitate the public, such as Smart Home. This study aims a smart home simulation model to move doors, TVs, and lights from voice instructions. Sound signals are processed using Mel-frequency Cepstrum Coefficients (MFCC) to perform feature extraction. Then, the voice is recognized by the speaker using a hierarchy Support Vector Machine (SVM). So that unregistered speakers are not processed or are declared not having access rights. For the process of recognizing spoken words such as "Open the Door","Close the Door","Turn on the TV","Turn off the TV","Turn on the Lights" and "Turn Off the Lights" are done using Backpropagation. The results showed that hierarchy SVM provided an accuracy of 71% compared to the single SVM of 45%.

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Voice-Based Door Access Control System Using the Mel Frequency Cepstrum Coefficients and Gaussian Mixture Model

Cited by 7 publications

References 5 publications

Residential load event detection in NILM using robust cepstrum smoothing based method

Residential load event detection in NILM using robust cepstrum smoothing based method

FPGA-based implementation of speech recognition for robocar control using MFCC

Speaker and Speech Recognition Using Hierarchy Support Vector Machine and Backpropagation

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