Signal processing has traditionally relied on carefully designed filters and algorithms tuned for specific applications. Deep learning offers a transformative approach by automatically learning representations and operations directly from data, enabling more adaptive and accurate processing.
Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been successfully applied to tasks such as noise reduction, denoising, and feature extraction in signals like audio, radar, and biomedical data. By leveraging large datasets and powerful training algorithms, these models can outperform classical techniques and adapt to complex, nonlinear signal characteristics.
However, integrating deep learning into signal processing pipelines requires careful consideration of computational resources, latency, and interpretability. Hybrid approaches that combine model-based insights with data-driven learning, such as deep unfolding networks, provide a promising direction for achieving high performance while maintaining transparency.