Markov Model Based Real Time Speaker Recognition using K-Means, Fast Fourier Transform and Mel Frequency Cepstral Coefficients

Borandağ, Emin

doi:10.18466/cbayarfbe.556936

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…A combination of sound processing and machine learning algorithms are applied in [35] for real-time speaker identification. They have utilized Markov chain classifier and real-time MFCC in their study.…”

Section: Related Workmentioning

confidence: 99%

Speaker Identification Approach for the Post-pandemic Era of Internet of Things

Saleema

Thampi

2021

Lecture Notes in Electrical Engineering

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With the rapid aggravation of COVID-19 pandemic, the organizations, industries, institutions, etc., are forced to rapidly adapt to social distancing by limiting physical contact and thereby limit the person to person contamination. The scenario of Internet of things in the post COVID era will be interesting indeed. Inorder to ensure public health, the social distancing and semi lockdown will continue in the foreseeable future, and therefore, the need of secure remote person authentication methods is being more and more critical especially in the Internet of things which is a multitude of networks consisting of a huge number of uniquely identifiable devices. As far as human_device authentication strategies are concerned, the one which needs less human involvement will be preferable in a post COVID-19 IoT scenario. Moreover, since the range of IoT devices may span from tiny sensors to complex machines, an authentication method which will be adaptable to each and every type of device will be more welcomed. Considering these facts, voice biometric authentication seems to be the most suitable one which can provide a balanced mix of security, adaptivity and convenience to such an advanced world of connectivity. Here, we introduce a lightweight text independent voice biometric method for IoT using extreme learning machines, and we perform a comparative analysis with a deep learning-based method of speaker identification using 3D convolutional neural networks. We have performed experimental study using different datasets and concluded that the extreme learning-based method is more suitable for IoT, considering the trade-off between the recognition accuracy and the training time requirements.

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Section: Related Workmentioning

confidence: 99%

Speaker Identification Approach for the Post-pandemic Era of Internet of Things

Saleema

Thampi

2021

Lecture Notes in Electrical Engineering

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“…Many studies used and implemented Fast Fourier transform and K-Means clustering in processing audio, the study [11,12] identifies a person from the characteristic of voice. All the frequencies (ALLFREQ) be clustered using K-Means, but before clustering the frequencies, we need to estimate the number of cluster K. In the study of [13], used Davies Bouldin Index in determining the optimal number of cluster K. Using Matlab function evalclusters, ALLFREQ be evaluated to find the optimal K.…”

Section: Fig 3 Process In Conversion and Classification Of Frequencymentioning

confidence: 99%

Noise Energy Harvester: Analysis and classification of frequency produced by boat engine

Opada¹,

Maravillas²,

Aliac³

2020

Ijasc

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Background/Objectives: A resonator plays a significant part in harnessing sound waves. An idea to use a resonator that only resonates on a specific frequency is hypothesized to harness sound efficiently. The primary objective of this study is to investigate the noise present in the boat engine room and be able to classify its resonant frequency and to know which of these frequencies occur most often. Methods/Statistical analysis: By using a sound recorder and a sound pressure level meter, the sound recorded from the idle state of the boat to disembarking and upon reaching the destination. Findings: Fast Fourier transform is applied to extract the frequencies of the recorded sound. Then the frequencies are evaluated using the Davies-Bouldin index to estimate the number of clusters and K-means clustering to classify the frequencies. Improvements/Applications: There are 2 clusters found and that Cluster 1 frequencies are from 17 Hz to 3295 Hz. Cluster 2 frequencies are from 6291 Hz to 8279 Hz. Among the available frequencies, 47 Hz, 155 Hz, and 186 Hz out weight all of the frequencies in terms of their occurrence, and among the three frequencies, 186 Hz has the highest amplitude.

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Markov Model Based Real Time Speaker Recognition using K-Means, Fast Fourier Transform and Mel Frequency Cepstral Coefficients

Cited by 2 publications

References 16 publications

Speaker Identification Approach for the Post-pandemic Era of Internet of Things

Speaker Identification Approach for the Post-pandemic Era of Internet of Things

Noise Energy Harvester: Analysis and classification of frequency produced by boat engine

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