Become a Readings Member to make your shopping experience even easier. Sign in or sign up for free!

Become a Readings Member. Sign in or sign up for free!

Hello Readings Member! Go to the member centre to view your orders, change your details, or view your lists, or sign out.

Hello Readings Member! Go to the member centre or sign out.

Deep Learning for Speech Signal Classification
Paperback

Deep Learning for Speech Signal Classification

$187.99
Sign in or become a Readings Member to add this title to your wishlist.

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.

Read More
In Shop
Out of stock
Shipping & Delivery

$9.00 standard shipping within Australia
FREE standard shipping within Australia for orders over $100.00
Express & International shipping calculated at checkout

MORE INFO
Format
Paperback
Publisher
LAP Lambert Academic Publishing
Date
26 March 2025
Pages
52
ISBN
9786208432799

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.

Read More
Format
Paperback
Publisher
LAP Lambert Academic Publishing
Date
26 March 2025
Pages
52
ISBN
9786208432799