Expectations for signal processing applications are getting higher. Engineers need to create applications that can intelligently respond to inputs or make predictions; often, this means incorporating AI systems into their designs.What does every AI-powered signal processing application need? A lot of representative signal data, a good network architecture (because signal data works particularly well with deep learning), and the right signal processing tools to turn that data into a source for automated learning.
The basics of deep learning for signal processing
Using datasets and labeling to train and validate models
Applying data augmentation and synthesis to improve the quality and quantity of training data
Creating inputs for deep networks
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