How is deep learning being applied to signal processing tasks?

Deep learning in signal processing: End-to-end learning (replace traditional pipelines, learn directly from raw signals), Learned representations (autoencoders, embeddings instead of hand-crafted features), Sequence modeling (RNN, LSTM, Transformer for temporal patterns), CNNs for spectrograms (treat as images), and Model-based deep learning (unrolled optimization algorithms with learned parameters). Applications: Speech recognition, audio synthesis, noise reduction, super-resolution, radar processing, and biomedical signal analysis. Advantages: Adapts to data, captures complex patterns. Challenges: Interpretability, data requirements, computational cost, and real-time constraints. Hybrid approaches combine domain knowledge with learning.