General Audio Signal Processing With Deep Learning - Rilegato

Xu, Kele

Rilegato

ISBN 10: 9819508355 ISBN 13: 9789819508358

Casa editrice: Springer Nature, 2026

Riassunto

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.  

Su questo libro

Informazioni sull'autore

Kele Xu is an Associate Professor at National University of Defense Technology China. His current research interests include Multimodal Machine Learning and Software Engineering. He is also interested in the applications of machine learning for audio signal processing, speech processing. During his part-time, he is a competition-driven researcher. I have won many data mining / machine learning competitions during last years, including ACM KDD Cup, Kaggle, Tianchi and CCF BDCI (CCF Big Data Computing Intelligence Contest). He is also a Kaggle Grandmaster.

Dalla quarta di copertina

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.  

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 9819508355
• ISBN 13: 9789819508358
• Rilegatura: Copertina rigida
• Numero di pagine: 650
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Germania