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K-means clustering is a simple unsupervised learning method. This method can be applied to implement color quantization in an image by finding clusters of pixel values. Another useful application would be automatic classification of phonemes in a speech signal by finding clusters of formant values for different speakers. | K-means clustering is a simple unsupervised learning method. This method can be applied to implement color quantization in an image by finding clusters of pixel values. Another useful application would be automatic classification of phonemes in a speech signal by finding clusters of formant values for different speakers. | ||
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Revision as of 14:51, 19 April 2017
Contents
K-Means Clustering Applications in Digital Signal Processing
by Sara Wendte
Introduction
K-means clustering is a simple unsupervised learning method. This method can be applied to implement color quantization in an image by finding clusters of pixel values. Another useful application would be automatic classification of phonemes in a speech signal by finding clusters of formant values for different speakers.
Theory
Color Quantization Application
Phoneme Classification Application
References
"14.2-Clustering-KMeansAlgorithm- Machine Learning - Professor Andrew Ng",
https://www.youtube.com/watch?v=Ao2vnhelKhI.
Stanford CS 221,"K Means",
http://stanford.edu/~cpiech/cs221/handouts/kmeans.html.
Purdue ECE 438, "ECE438 - Laboratory 9: Speech Processing (Week 1)", October 6, 2010,
https://engineering.purdue.edu/VISE/ee438L/lab9/pdf/lab9a.pdf.
