General Audio Signal Processing with Deep Learning | 117.91 MB
Title: General Audio Signal Processing with Deep Learning
Author: Kele Xu, Jisheng Bai, Boqing Zhu, Qisheng Xu, Yi Su, Mou Wang
Category: Nonfiction, Science & Nature, Mathematics, Statistics, Computers, Advanced Computing, Artificial Intelligence, Technology, Engineering
Language: English | 697 Pages | ISBN: 1119279895
Description:
Dive into the cutting-edge integration of deep learning with audio signal processing in this authoritative guide. Designed for audio engineers, data scientists, and tech enthusiasts, this book demystifies the complex world of deep neural networks, including CNNs and RNNs, and their applications in speech recognition, music transcription, and sound event detection.
Explore the practical side of deep learning with hands-on tutorials using TensorFlow and PyTorch, building your intuition for model architectures and hyperparameter tuning. Gain insights into real-world deployment challenges, from data preprocessing to model evaluation, interpretability, and scalability. Industry case studies and best practices illuminate the path to building efficient and effective deep learning-based audio systems.
This book empowers you with the knowledge to leverage the full potential of deep learning in audio processing, offering a comprehensive resource for tackling sophisticated audio tasks. Whether you’re a researcher, engineer, or enthusiast, this guide is your key to mastering the synergy of audio signal processing and deep learning, ensuring you approach audio-related challenges with confidence and proficiency.
Dive into the cutting-edge integration of deep learning with audio signal processing in this authoritative guide. Designed for audio engineers, data scientists, and tech enthusiasts, this book demystifies the complex world of deep neural networks, including CNNs and RNNs, and their applications in speech recognition, music transcription, and sound event detection.
Explore the practical side of deep learning with hands-on tutorials using TensorFlow and PyTorch, building your intuition for model architectures and hyperparameter tuning. Gain insights into real-world deployment challenges, from data preprocessing to model evaluation, interpretability, and scalability. Industry case studies and best practices illuminate the path to building efficient and effective deep learning-based audio systems.
This book empowers you with the knowledge to leverage the full potential of deep learning in audio processing, offering a comprehensive resource for tackling sophisticated audio tasks. Whether you’re a researcher, engineer, or enthusiast, this guide is your key to mastering the synergy of audio signal processing and deep learning, ensuring you approach audio-related challenges with confidence and proficiency.
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