Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments 1st Edition

Capture clearer speech from the world’s noisiest places with Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments, 1st Edition by Xiao‑Lei Zhang. This authoritative text addresses the urgent challenge of making machines and devices understand human voice reliably across urban streets, crowded venues, reverberant rooms, and industrial sites.

Discover how modern deep learning transforms speech signal processing: from robust feature learning and end-to-end recognition to speech enhancement, source separation, dereverberation, and microphone-array beamforming. Zhang blends theory with practical insight, explaining convolutional and recurrent architectures, attention mechanisms, multi-channel methods, and evaluation metrics that matter in real-world deployments. Case studies and application-focused discussions highlight use cases in teleconferencing, voice assistants, hearing aids, robotics, and telecommunications—making it an essential resource for researchers, engineers, and graduate students worldwide.

Why this book matters: it bridges academic rigor and industry needs, offering clear explanations of complex algorithms and guidance for tackling diverse acoustic environments. Whether you’re developing noise-robust ASR systems, improving audio quality for consumer devices, or researching speech enhancement, this volume equips you with modern techniques and the conceptual tools to innovate.

Ideal for international audiences seeking practical, cutting-edge knowledge, Xiao‑Lei Zhang’s work is a go-to reference for anyone advancing speech technology in real-world settings. Add this definitive guide to your library and elevate your projects with proven deep-learning strategies for challenging acoustic conditions. Order your copy today and start turning noisy data into intelligible speech.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.