Speaker diarization system using MKMFCC parameterization and WLI-fuzzy clustering

Ramaiah, V. Subba; Rao, R. Srinivasa

doi:10.1007/s10772-016-9384-y

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…The need for the multiple kernel Mel frequency cepstral coefficient (MKMFCC) feature [41] is interpreted that extracts all possible phonetic components from the audio signal through considering the low and high energy frames of the audio signal. [43].…”

Section: Multiple Kernel Mel Frequency Cepstral Coefficient Featurementioning

confidence: 99%

“…Multiple kernel Mel frequency cepstral coefficient feature: The need for the multiple kernel Mel frequency cepstral coefficient (MKMFCC) feature [41] is interpreted that extracts all possible phonetic components from the audio signal through considering the low and high energy frames of the audio signal. Let us represent the MKMFCC features as

F_{2}

and the dimension of the features is denoted as

[1 \times 13]

.…”

Section: Proposed Audio Classification Strategy Using the Optimization Enabled Svm Classifiermentioning

confidence: 99%

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Audio classification using grasshopper‐ride optimization algorithm‐based support vector machine

Singh

Jaiswal

2021

IET Circuits, Devices & Syst

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The accurate and robust detection of the audio has been widely grown as the speech technology in the area of audio forensics, speech recognition, and so on. However, in real time, it is a challenge to deal with the massive data arriving from distributed sources. Thus, the study introduces a method that effectively deals with the data from the distributed sources using the map-reduce framework (MRF). The map and reduce function in MRF aim at feature extraction and audio classification. The robust classification using the proposed grasshopper-ride optimization algorithm-based support vector machine (G-ROA-based SVM) uses the features, such as multiple kernel Mel frequency cepstral coefficients, spectral flux, spectral kurtosis, and delta-amplitude modulation spectrogram. The proposed G-ROA is the integration of ROA and grasshopper optimization algorithm in tuning the optimal weights of SVM and also, the kernel function in SVM is modified using the Gaussian radial basis function, Gaussian kernel, and polynomial kernels. The experimentation of the proposed method is done using two datasets, namely TUT sound event 2017 dataset and ESC dataset. TUT sound event 2017 dataset consists of eight audio recordings from a single acoustic scene. ESC dataset consists of three parts and 252,400 recordings. The analysis reveals that the proposed audio classification acquired the maximal accuracy of 0.96, minimal false alarm rate, and false rejection rate of 0.022 and 0.0119, respectively. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Multiple Kernel Mel Frequency Cepstral Coefficient Featurementioning

confidence: 99%

F_{2}

and the dimension of the features is denoted as

[1 \times 13]

.…”

Section: Proposed Audio Classification Strategy Using the Optimization Enabled Svm Classifiermentioning

confidence: 99%

Audio classification using grasshopper‐ride optimization algorithm‐based support vector machine

Singh

Jaiswal

2021

IET Circuits, Devices & Syst

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“…Here, MKMFCC [9] is selected as a feature parameter which is Mel frequency scale, which extracts entire phonetic component features and it is not able to differentiate frequencies above 1 kHz as same as the human. In this approach, coefficients of MFCC are attained by exploiting 2 diverse kernel functions.…”

Section: Mkmfccmentioning

confidence: 99%

Hybrid Particle Swarm Optimization-Deep Neural Network Model for Speaker Recognition

2020

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Nowadays, speaker recognition is considered as a current research topic. Moreover, the voice biometrics which is attained from the speaker's behavior or physical related features offers a pattern of data that contains sensitive information regarding the speaker. The efficiency of speaker recognition systems is observed to minimize expeditiously because of the mismatch incidence, such as noise and channel degradations. With the aspire to promise security and effectual recognition, a Hybrid Particle Swarm Optimization-based Deep Neural Network (Hybrid PSO-based DNN classifier) is used to identify a speaker for that the frequency-dependent features, like MKMFCC, autocorrelation, and spectral skewness, are exploited. The classification is done by exploiting the DNN classifier based on feature extraction and classifier is performed optimally by exploiting the proposed Particle Swarm Optimization. Finally, the simulation analysis of the proposed technique is compared with the LM, SVM, GMM, and BSW. It shows the performance of the proposed technique outperforms the conventional techniques concerning the accuracy, FAR FRR.

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“…The proposed method is implemented in MATLAB software that runs in PC with Windows-8 OS and the data used for the analysis of the proposed chronological ASO algorithm is taken from ELSDSR corpus (ELSDSR database) [12].…”

Section: A Experimental Setupmentioning

confidence: 99%

Katsinovas Framework Prototype Apps Hardware as based Innovation Readiness Level

Waliulu*,

Saptono,

Suryani

et al. 2020

IJITEE

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Internet of Things (IoT) is a developing technology used for enhancing the communication and providing flexibility to humans. The data packets are transmitted through better routing path using the IoT nodes in the network. This research presents an effective optimization algorithm for selecting the effective routing path for the transmission of the speech signal to the destination effectively. The optimization algorithm namely, chronological atom search optimization (chronological ASO) algorithm is developed for selecting the routing path. The chronological ASO algorithm is developed by integrating the chronological concept in ASO algorithm. After the initialization of the network, the effective routing path is selected using the fitness parameters, such as energy, delay and distance. Then, the speech signal is transmitted from the source node to the destination node by selecting the routing path having maximum fitness function. The performance analysis of the proposed chronological ASO algorithm is done based on the metrics, such as energy, throughput, and PDR. When compared to the existing methods, the proposed method provided a maximal energy of 0.2375, maximal PDR of 46.1538, and maximal throughput of 0.0792 for 50 nodes and obtained a maximal energy of 0.1381, maximal PDR of 32.0988, and maximal throughput of 0.0395, respectively.

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Speaker diarization system using MKMFCC parameterization and WLI-fuzzy clustering

Cited by 11 publications

References 23 publications

Audio classification using grasshopper‐ride optimization algorithm‐based support vector machine

Audio classification using grasshopper‐ride optimization algorithm‐based support vector machine

Hybrid Particle Swarm Optimization-Deep Neural Network Model for Speaker Recognition

Katsinovas Framework Prototype Apps Hardware as based Innovation Readiness Level

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