SPEECH PROCESSING USING KALMAN FILTER
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Aim of our Project
• The primary goal is to design a MATLAB based simulator for processing of speech signal with the aid of the Kalman filtering technique .To obtain a reconstructed speech signal, which is similar to the input signal.
• To achieve these results, sample speeches were obtained. These were modeled as an autoregressive (AR) process and represented in the state – space domain by the Kalman filter.
Introduction to Filter
 Definition of Filter.
 Block diagram:
Classification of Filters.
 Analog Filters.
 Digital Filters
Digital Filters
 Process of a Digital Filter.
Its Advantages:
 A digital filter is programmable.
 Easy to design, test and implement.
 It can even handle low frequency signals accurately.
Popular Digital Filters
• LMS Filter.
• Wiener Filter
• Kalman Filter.
Advantages of Kalman Filter over Wiener Filter
 The Kalman filter requires less additional mathematical preparation to learn for the modern control engineering student, compared to the Wiener filter.
 The Kalman Filter algorithm is implementable on a digital computer, which is capable o f greater accuracy when compared to Wiener Filter.
 Stationary properties of the Kalman filter are not required for the deterministic dynamics or random processes.
Kalman Filter Perspectives
• It is only a tool.
• It is a computer program.
• It is a complete statistical characterization of an estimation problem.
• In a limited context, it is a learning method.
But the main purpose is estimation and performance analysis of estimators.
Up gradation process
• Features of Up gradation process
• Simulated results of Kalman filter
Overview of simulation process.
• Basic idea of approach.
• Adaptability of Kalman filter.
• Simulated results of Kalman filter
• Coefficients at different iterations for randomly varying discrete signals
• Result of speech samples at
Q=1 x 10-3 and R=0.1 (S180)
Cross Correlation
Definition.
• Purpose of implementation.
• Condition for similarity
• Cross Correlation results for speech samples (s180)
• Result of speech samples at
Q=1 x 10-3 and R=0.1 (S680)
• Cross Correlation results for speech samples (s680)
• Result of speech samples at
Q=1 x 10-3 and R=0.1 (S1180)
• Cross Correlation results for speech samples (s1180)
• Parameter tuning to obtain optimal estimation
(for S680 here Q=1×10^-6)
• Cross Correlation results for speech samples (s680)
• Parameter tuning to obtain optimal estimation
(for S1180 here Q=1×10^-8)
• Cross Correlation results for speech samples (s1180)
Conclusion
 In this thesis, an implementation of employing Kalman filtering to speech processing had been developed. The simulated results had proven that the Kalman filter indeed has the ability to estimate accurately.
 The results have also shown that Kalman filter could be tuned to provide optimal performance.
 Moreover, a test for cross correlation had also been conducted during this thesis for measuring the similarity of the input and output speech signals. This test is of necessity for the reason that different signals are bound to be similar but not identical.
Future developments.
 Speech compression.
 Quality of speech.
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