A Review on Artificial Intelligence Applications in Modern Signal Processing Systems
Keywords:
Artificial intelligence, Deep learning, Feature extraction, Intelligent systems, Signal processing, Wavelet transformAbstract
Signal processing is a core discipline in electronics and communication engineering that deals with the acquisition, analysis, transformation, and interpretation of signals such as speech, images, biomedical signals, and sensor data. Conventional signal processing techniques, including Fourier analysis, filtering, and linear system modelling, have been widely adopted due to their solid mathematical foundations and computational efficiency. While these methods perform well for structured and stationary signals, they often struggle with real-world data that is noisy, non-linear, time-varying, and high-dimensional. The rapid growth in data complexity and volume has driven the need for more adaptive and intelligent signal processing approaches. Artificial Intelligence (AI), particularly machine learning and deep learning, has emerged as an effective solution to address the limitations of traditional methods. Machine learning algorithms enable systems to automatically learn patterns from data, enhancing performance in tasks such as classification, detection, and prediction. Deep learning models, including convolutional and recurrent neural networks, further improve signal processing by automatically extracting meaningful spatial and temporal features from raw signals, reducing dependency on handcrafted features. In addition, advanced signal representation techniques such as wavelet transforms provide joint time-frequency analysis, making them suitable for non-stationary signal processing. The integration of wavelet-based methods with AI models enhances robustness and accuracy in complex and noisy environments. This paper presents a comprehensive review of AI applications in modern signal processing, covering speech and audio processing, image and video analysis, biomedical signal interpretation, smart sensor networks, and predictive maintenance. Key challenges related to computational complexity, data dependency, interpretability, and ethical issues are also discussed. The study concludes that AI-integrated signal processing systems offer intelligent, scalable, and robust solutions for complex real-world engineering applications.
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