As we navigate our everyday life, we are continuously parsing through a cacophony of sounds that are constantly impinging on our senses. This ability to sieve through everyday sounds and pick-out signals of interest may seem intuitive and effortless, but it is a real feat that involves complex brain networks that balance the sensory signal with our goals, expectations, attentional state and prior knowledge (what we hear, what we want to hear, what we expect to hear, what we know). A similar challenge faces computer systems that need to adapt to dynamic inputs, evolving objectives and novel surrounds. A growing body of work in neuroscience has been amending our views of processing in the brain; replacing the conventional view of ‘static’ processing with a more ‘active’ and malleable mapping that rapidly adapts to the task at hand and listening conditions. After all, humans and most animals are not specialists, but generalists whose perception is shaped by experience, context and changing behavioral demands. The talk will discuss theoretical formulations of these adaptive processes and lessons to leverage attentional feedback in algorithms for detecting and separating sounds of interest (e.g. speech, music) amidst competing distractors.