Signal processing, the art and science of extracting information from signals, has been a cornerstone of technological advancement across countless domains, from telecommunications and medical imaging to audio analysis and autonomous systems. Traditionally, its foundation lay in mathematical transforms like Fourier and Wavelet, filtering, and statistical analysis. However, as we navigate an era of unprecedented data generation, ubiquitous sensing, and the rise of artificial intelligence, the future of signal processing techniques is set for a profound evolution, moving beyond conventional algorithms to embrace adaptive, intelligent, and context-aware methodologies. LINK
One of the most transformative forces shaping the future of signal processing is the deep integration with Artificial Intelligence and Machine Learning (AI/ML), particularly deep learning. Traditional signal processing often relies on hand-crafted features derived from domain knowledge. In contrast, deep neural networks possess the ability to automatically learn optimal features directly from raw data, leading to superior performance in tasks like speech recognition, image classification, and anomaly detection. Future techniques will increasingly leverage end-to-end deep learning models for signal analysis, synthesis, and enhancement, moving from purely model-driven or data-driven approaches to hybrid methods that combine the strengths of both. This includes techniques for sparse signal reconstruction, compressed sensing, and robust noise cancellation, all enhanced by AI.
Another critical trend is the shift towards edge computing and distributed processing. As the Internet of Things (IoT) expands, billions of sensors and devices generate vast amounts of data at the network's periphery. Processing this data efficiently on-device, rather than solely relying on cloud offloading, requires highly optimized and low-power signal processing algorithms. This necessitates advancements in energy-efficient hardware accelerators (like TinyML chips), and novel algorithms for real-time processing under constrained resources, ensuring minimal latency and enhanced privacy. Techniques will focus on model compression, quantization, and efficient inference on resource-limited embedded