scholarly journals An Improved Speech Segmentation and Clustering Algorithm Based on SOM and K-Means

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Nan Jiang ◽  
Ting Liu
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Speech Segmentation ◽  
Endpoint Detection ◽  
Number Of Clusters ◽  
Initial Cluster ◽  
Neural Network Algorithm ◽  
Speech Endpoint Detection ◽  
Double Threshold ◽  
Self Organizing

This paper studies the segmentation and clustering of speaker speech. In order to improve the accuracy of speech endpoint detection, the traditional double-threshold short-time average zero-crossing rate is replaced by a better spectrum centroid feature, and the local maxima of the statistical feature sequence histogram are used to select the threshold, and a new speech endpoint detection algorithm is proposed. Compared with the traditional double-threshold algorithm, it effectively improves the detection accuracy and antinoise in low SNR. The k-means algorithm of conventional clustering needs to give the number of clusters in advance and is greatly affected by the choice of initial cluster centers. At the same time, the self-organizing neural network algorithm converges slowly and cannot provide accurate clustering information. An improved k-means speaker clustering algorithm based on self-organizing neural network is proposed. The number of clusters is predicted by the winning situation of the competitive neurons in the trained network, and the weights of the neurons are used as the initial cluster centers of the k-means algorithm. The experimental results of multiperson mixed speech segmentation show that the proposed algorithm can effectively improve the accuracy of speech clustering and make up for the shortcomings of the k-means algorithm and self-organizing neural network algorithm.

A New Approach to Machinery Monitoring and Diagnostics Using Self-Organizing Maps

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Trend Analysis ◽  
New Approach ◽  
Machine System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Monitoring And Diagnostics ◽  
Self Organizing

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

The Algorithm of Quadratic Junction Neural Network

2013 ◽  
Vol 462-463 ◽  
pp. 438-442
Author(s):  
Ming Gu
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Artificial Data ◽  
Data Set ◽  
Number Of Clusters ◽  
Learning Rules ◽  
The Neural Network ◽  
Hierarchical Clustering Algorithm ◽  
Clustering Data ◽  
Structure Properties

Neural network with quadratic junction was described. Structure, properties and unsupervised learning rules of the neural network were discussed. An ART-based hierarchical clustering algorithm using this kind of neural networks was suggested. The algorithm can determine the number of clusters and clustering data. A 2-D artificial data set is used to illustrate and compare the effectiveness of the proposed algorithm and K-means algorithm.

Multivariable Trend Analysis Using Neural Networks for Intelligent Diagnostics of Rotating Machinery

1997 ◽  
Vol 119 (2) ◽  
pp. 378-384 ◽  
Author(s):  
S. Zhang ◽  
R. Ganesan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Trend Analysis ◽  
Condition Monitoring ◽  
Diagnostic Procedure ◽  
On Line ◽  
Neural Network Algorithm ◽  
Intelligent Diagnostics ◽  
Bearing System ◽  
Self Organizing

The objective of this paper is the development of an efficient intelligent diagnostic procedure that considers several diagnostic indices for the quantification of developing faults and for monitoring machine condition. In this procedure, the condition monitoring is performed based on the on-line vibration measurements, and further, the fault quantification is formulated into a multivariate trend analysis. Self-organizing neural networks are then deployed to perform the multivariable trending of the fault development. The attributes for the disordering of “knots” in the trend analysis are determined. The disordering of neural network units is then eliminated by suitably altering the self-organizing neural network algorithm. Applications of this diagnostic procedure to the condition monitoring and life estimation of a bearing system are fully developed and demonstrated. The efficiency and advantages of the intelligent diagnostic procedure in precisely monitoring and quantifying the fault development are systematically brought out considering this bearing system.

scholarly journals IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)

2017 ◽  
Vol 4 (2) ◽  
pp. 198
Author(s):  
Fatma Agus Setyanngsih
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Self Organizing Map ◽  
Rainfall Prediction ◽  
The Neural Network ◽  
Network Time ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
The City ◽  
Self Organizing

<p><em>The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 124 datas, with MSE value 0,0148.</em><strong> </strong></p><p><strong><em>Keywords</em></strong><em>: </em><em>Rainfall, Neural Network, Time Series, Self Organizing Map</em></p><p><em>Prediksi untuk mengetahui curah hujan yang terjadi di Pontianak sangat dibutuhkan salah satunya yaitu menggunakan algoritma jaringan syaraf tiruan dengan pengelompokkannya menggunakan SOM (Self Organizing Map) dengan data yang digunakan adalah data di bulan januari tahun 2010-2013. Tujuan dari penelitian ini adalah untuk mengetahui prediksi curah hujan di kota Pontianak dengan parameter suhu udara, kelembababn relative, tekanan udara dan kecepatan angin. Hasil penelitian menunjukkan bahwa nilai MSE ini didapatkan saat jaringan mempelajari data prediksi pada bulan januari di tahun 2010 sampai tahun 2013 dengan menggunakan proses pembelajaran JST SOM dengan jumlah neuron 1 dan menggunakan 124 data, dengan nilai MSE 0,0148. </em></p><p><em></em><em><strong><em>Kata kunci</em></strong><strong><em>:</em></strong><em> </em><em>Curah Hujan, Jaringan Syaraf Tiruan, Time Series, Self Organizing Map</em></em></p>

scholarly journals Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps

2016 ◽  
Vol 25 (43) ◽  
pp. 73-82
Author(s):  
Álvaro David Orjuela-Cañón ◽  
Hugo Fernando Posada-Quintero
Keyword(s):  
Neural Networks ◽  
Clustering Algorithm ◽  
Mel Frequency Cepstral Coefficients ◽  
Self Organizing Maps ◽  
Number Of Clusters ◽  
Supervised Training ◽  
Computer Aided ◽  
Unsupervised Training ◽  
Cepstral Coefficients ◽  
Self Organizing

This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. Both approaches were used in this work to compare the utility of this tool in lung signals studies. Results showed that with a supervised training, the classification reached rates of 85 % in accuracy. Unsupervised training was used for clustering tasks, and three clusters was the most adequate number for both supervised and unsupervised training. In general, SOM models can be used in lung signals as a strategy to diagnose systems, finding number of clusters in data, and making classifications for computer-aided decision making systems.

Validity Test of Self-Organizing Map (SOM) and K-Means Algorithm for Employee Grouping

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Titik Susilowati ◽  
Dedy Sugiarto ◽  
Is Mardianto
Keyword(s):  
Clustering Algorithm ◽  
The Self ◽  
Self Organizing Map ◽  
Number Of Clusters ◽  
Internal Validation ◽  
Validity Test ◽  
Validation Test ◽  
Silhouette Index ◽  
Employee Attendance ◽  
Self Organizing

Managing employee work discipline needs to be done to support the development of an organization. One way to make it easier to manage employee work discipline is to group employees based on their level of discipline. This study aims to group employees based on their level of discipline using the Self Organizing Map (SOM) and K-Means algorithm. This grouping begins with collecting employee attendance data, then processing attendance data where one of them is determining the parameters to be used, then ending by implementing the clustering algorithm using the SOM and K-Means algorithms. The results of grouping that have been obtained from the implementation of the SOM and K-Means algorithms are then validated using an internal validation test consisting of the Dunn Index, the Silhouette Index and the Connectivity Index to obtain the best number of clusters and algorithms. The results of the validation test obtained 3 best clusters for the level of discipline, namely the disciplinary cluster, the moderate cluster and the undisciplined cluster.

Sign in / Sign up

Export Citation Format

Share Document