Digital signal processing and machine learning

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. In digital electronics, a digital signal is represented as a pulse train,^[1][2] which is typically generated by the switching of a transistor.^[3] Digital Signal Processing (DSP) has experienced considerable advancements over recent decades, largely due to innovations in digital computing and integrated circuit technology.

Approximately thirty years ago, digital computers and their hardware were typically large, expensive, and primarily utilized for general-purpose applications in scientific and business contexts, often without real-time processing capabilities. The progression from medium-scale integration (MSI) to large-scale integration (LSI) and eventually to very-large-scale integration (VLSI) has facilitated the development of smaller, faster, and more cost-effective digital computers, along with specialized DSP hardware. These advancements in digital circuits now enable the design of highly capable digital systems, allowing the execution of complex DSP tasks that were once impractical or prohibitively expensive to manage with analog systems.

Consequently, many signal processing tasks that were traditionally performed using analog methods are now efficiently handled by digital hardware, offering significant advantages in terms of cost, reliability, and flexibility. This transition from analog to digital processing has expanded the range of DSP applications and enhanced performance capabilities across various fields, including telecommunications, medical imaging, and audio processing.^[4]

Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Quick progress in the field of deep learning, beginning in 2010s, allowed neural networks to surpass many previous approaches in performance.^[5] Machine learning, a subfield of artificial intelligence (AI), enables computers and computer-controlled systems to perform tasks that require intelligent behavior, such as pattern recognition, data interpretation, and decision-making. It allows computers to address complex problems where establishing traditional, rule-based models would be inefficient or impractical.

Machine learning employs various techniques, including supervised, unsupervised, and reinforcement learning, to enable systems to learn from data and make predictions or classifications without being explicitly programmed with the models they aim to apply. Machine learning has gained widespread success and is now a fundamental component of numerous applications, including image recognition, natural language processing, autonomous systems, and predictive analytics. As a branch of computer science, it focuses on the development of algorithms that allow computers to identify patterns and understand data, mimicking certain aspects of human cognitive abilities. The adoption of machine learning has significantly expanded the capabilities of AI systems, contributing to its integration into a wide range of fields and technologies.^[6]