The Art of Signal Processing: Markov Model Approach
Keywords:
Deep learning integration, Hidden markov models, Image segmentation, Markov models, Non-stationary signals, Probabilistic modeling, Signal processing, Speech recognitionAbstract
Signal processing serves as an essential backbone across different domains, including communication systems, healthcare applications, and artificial intelligence components. Both Fourier transforms and wavelet analysis have traditionally been applied to signal decomposition tasks while extracting fundamental features from the data. The deterministic methods fail to handle changing or non-stationary signals that evolve over time, especially for data types such as speech, video, or sensor readings. Markov Models have become a superior alternative to overcome this issue. Using the Markov approach leads to exceptional results when working with data sets that demonstrate sequential dependencies together with uncertain elements. This paper explains the application of Markov Models through an examination of Hidden Markov Models (HMMs) that implement probabilistic signal evolution analysis on time-series data. This paper reviews Markov Model fundamentals while exploring their practical use in speech recognition, together with image segmentation applications, including the problems encountered in practical implementation. The paper examines potential improvements that will result from merging deep learning approaches with traditional Markov Models to improve their operational capabilities.
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