scholarly journals Pengenalan Jenis Kelamin Manusia Berbasis Suara Menggunakan MFCC dan GMM

2021 ◽  
Vol 1 (1) ◽  
pp. 28-33
Author(s):  
Faisal Dharma Adhinata ◽  
Diovianto Putra Rakhmadani ◽  
Alon Jala Tirta Segara
Keyword(s):  
Detection System ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Male Voice ◽  
Biometric Data ◽  
Human Voice ◽  
Voice Detection ◽  
Voice Data ◽  
Biometric Information

Biometric information that exists in humans is unique from one human to another. One of the biometric data that is easily obtained is the human voice. The human voice is identic data that can differentiate between individuals. When we hear human voices directly, it is easy for our ears to tell the person who is speaking is male or female. But sometimes male voices can resemble girls and vice versa. Therefore, we propose a human voice detection system through Artificial Intelligence (AI) in machine learning. In this study, we used the Mel Frequency Cepstrum Coefficients (MFCC) method to extract human voice features and Gaussian Mixture Models (GMM) for the classification of female or male voice data. The experiment results showed that the system built was able to detect human gender through biometric voice data with an accuracy of 81.18%.

Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models

2006 ◽  
Vol 27 (10) ◽  
pp. 935-951 ◽  
Author(s):  
Felicity R Allen ◽  
Eliathamby Ambikairajah ◽  
Nigel H Lovell ◽  
Branko G Celler
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture

scholarly journals New Families in our Solar Neighborhood: Applying Gaussian Mixture Models for Objective Classification of Structures in the Milky Way and in Simulations

2021 ◽  
Vol 921 (2) ◽  
pp. 106
Author(s):  
Farnik Nikakhtar ◽  
Robyn E. Sanderson ◽  
Andrew Wetzel ◽  
Sarah Loebman ◽  
Sanjib Sharma ◽  
...  
Keyword(s):  
Mixture Models ◽  
Milky Way ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Solar Neighborhood ◽  
Objective Classification

A Canonicalization of Distinctive Phonetic Features to Improve Arabic Speech Recognition

2019 ◽  
Vol 105 (6) ◽  
pp. 1269-1277 ◽  
Author(s):  
Yousef A. Alotaibi ◽  
Sid-Ahmed Selouani ◽  
Mohammed Sidi Yakoub ◽  
Yasser Mohammed Seddiq ◽  
Ali Meftah
Keyword(s):  
Markov Models ◽  
Language Model ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Model Systems ◽  
Phoneme Recognition ◽  
Phonetic Features ◽  
Language Context

The robustness of speech classification and recognition systems can be improved by the adoption of language distinctive phonetic feature (DPF) elements that can increase the effective characterization of a speech signal. This paper presents the results of applying Hidden Markov Models (HMMs) that perform Arabic phoneme recognition in conjunction with the inclusion and classification of their DPF element classes. The research focuses on classifying Modern Standard Arabic (MSA) phonemes within isolated words without a language context. HMM-based phoneme recognition is tested using 8, 16, and 32 HMM Gaussian mixture models. The monophone configuration is designed with consideration of 2-gram language model to evaluate the inherent performance of the system. The overall correct rates for classifying DPF element classes for the three versions of HMM systems are 83.29% 88.96%, and 92.70% for 8, 16, and 32 HMM Gaussian mixture model systems, respectively.

scholarly journals Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database

2021 ◽  
Vol 11 (15) ◽  
pp. 7149
Author(s):  
Ji-Yeoun Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Linear Prediction ◽  
Detection System ◽  
Mel Frequency Cepstral Coefficients ◽  
Learning Methods ◽  
Acoustic Measures ◽  
Voice Detection ◽  
Voice Data ◽  
Pathological Voice

This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed between the normal and the pathological voice signals for normalized skewness (p = 0.000) and kurtosis (p = 0.000), except for normalized kurtosis (p = 0.051) that was estimated in the /u/ samples in women. These parameters are useful and meaningful for classifying pathological voice signals. The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter in the /u/ vowel in men. The second-best performance, 80.77%, was obtained with a combination of the FNN classifier, MFCCs, and HOSs for the /i/ vowel samples in women. There was merit in combining the acoustic measures with HOS parameters for better characterization in terms of accuracy. The combination of various parameters and deep learning methods was also useful for distinguishing normal from pathological voices.

Autonomous Anomaly Detection System for Crime Monitoring

2019 ◽  
Vol 16 (8) ◽  
pp. 3410-3418
Author(s):  
Muhammed Shuaau ◽  
Ka Fei Thang
Keyword(s):  
Anomaly Detection ◽  
Trajectory Analysis ◽  
Detection System ◽  
False Positive Rate ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Asia Pacific ◽  
Detection Accuracy ◽  
Positive Rate ◽  
Reception Area

Autonomous anomaly detection has attracted significant amount of attention in the past decade due to increased security concerns all around the world. The volume of data reported by surveillance cameras has outrun human capacity and there exists a greater need for anomaly detection systems for crime monitoring. This project proposes a solution to this problem in a reception area context by using trajectory analysis. Trajectory extraction is proposed by using Gaussian Mixture Models and Kalman Filter for data association. Then trajectory analysis is performed on extracted trajectories to detect four different anomalies which are entering staff area, running, loitering and squatting down. The proposed anomaly detection method is tested on datasets recorded at Asia Pacific University’s reception area. The proposed algorithms were able to achieve a detection accuracy of 89% and a false positive rate of 4.52%. The results presented show the effectiveness of the proposed method.

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