Deep learning networks have gained a reputation for being a very useful technique for image classification, but what value do they bring to signal data?
Whether you are working with audio or sensor data, deep learning networks, such as convolutional neural networks (CNNs), can do everything a mathematical model can do without requiring you to be an expert on signal processing.
Applied correctly, deep learning networks make signal processing tasks faster, more efficient, and more accurate.
Read this white paper to review some deep learning basics and see three examples where deep learning can add value to signal processing applications:
- Classifying speech audio files using a CNN
- Predicting remaining useful life (RUL) using a long short-term memory (LSTM) network
- Denoising speech with a fully connected neural network
