Speech signal classification plays a crucial role in speech recognition, speaker identification, emotion detection, and audio processing. This book provides a comprehensive guide to leveraging deep learning techniques—specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks—for effective speech signal classification.Key Topics Covered:Fundamentals of Speech Processing – Understanding speech signals, spectrograms, and feature extraction techniques like MFCCs. Introduction to Deep Learning – Overview of neural networks, CNNs for feature extraction, and LSTMs for capturing temporal dependencies.CNN-LSTM Hybrid Model – A step-by-step approach to combining CNNs and LSTMs for improved speech classification accuracy.
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Dr. Ragupathy K holds a Ph.D. in Mechanical Engineering and is a distinguished faculty member at Agni College of Technology. His research expertise lies in Aluminium Metal Matrix Composite materials, focusing on enhancing their properties for advanced engineering applications.
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Taschenbuch. Zustand: Neu. Deep Learning for Speech Signal Classification | A CNN-LSTM Approach | Ragupathy K. (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208432799 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Artikel-Nr. 132489482
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