Machine hearing mirrors human perception by comparing feature vectors against known patterns

Evidence

The theory and techniques of pattern recognition have developed through research into machine vision and machine hearing. In pattern recognition, information captured from the real world (for example, a visual or auditory signal) is pre-processed and transformed into a vector of relevant features. The acquired feature vector is then compared to a set of known feature vectors using a suitable algorithm, with the goal of recognizing (classifying) the unknown pattern. Pattern recognition often requires the formation of quantitative metrics for distance or similarity (compare with the principles of grouping). — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 1327

Categorization

• XREF: Connects to modern deep-learning audio classification and speech recognition pipelines that rely on feature extraction.