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Hands-On Deep Learning for IoT

You're reading from  Hands-On Deep Learning for IoT

Product type Book
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Pages 308 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Profile icon Dr. Mohammad Abdur Razzaque
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
View More author details
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

Collecting data

Data collection for ASR is a challenging task for many reasons, including privacy. Consequently, open source datasets are limited in number. Importantly, these datasets may not be easy to access, may have insufficient data/speakers, or may be noisy. In this context, we decided to use two different datasets for the two use cases. For the voice-driven controlled smart light, we are using Google’s speech command datasets, and for use case two, we can scrap data from one of three popular open data sources, LibriVox, LibriSpeech ASR, corpus, voxceleb, and YouTube.

Google's speech command dataset includes 65,000 one-second long utterances of 30 short words, contributed to by thousands of different members of the public through the AIY website. The dataset offers basic audio data on common words such as On, Off, Yes, digits, and directions, but this can be...

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