Machine learning in signal processing

Machine learning in signal processing involves using algorithms that automatically analyze and interpret signals—like audio, images, or sensor data—by learning patterns from examples. Instead of programming explicit rules, the system trains on data to recognize features such as speech, images, or system anomalies. This enables tasks like noise reduction, speech recognition, or fault detection to improve over time as the model learns. Essentially, machine learning enhances the ability of signal processing systems to adapt, interpret complex data, and perform tasks more accurately and efficiently without manual programming for every specific scenario.