Nearest-neighbor automatic sound classification with a wordNet taxonomy

Abstract

Sound engineers need to access vast collections of sound efects for their film and video productions. Sound efects providers rely on text-retrieval techniques to offer their collections. Currently, annotation of audio content is done manually, which is an arduous task. Automatic annotation methods, normally fine-tuned to reduced domains such as musical instruments or reduced sound effects taxonomies, are not mature enough for labeling with great detail any possible sound. A general sound recognition tool would require first, a taxonomy that represents the world and, second, thousands of classifiers, each specialized in distinguishing little details. We report experimental results on a general sound annotator. To tackle the taxonomy definition problem we use WordNet, a semantic network that organizes real world knowledge. In order to overcome the need of a huge number of classifiers to distinguish many different sound classes, we use a nearest-neighbor classifier with a database of isolated sounds unambiguously linked to WordNet concepts. A 30% concept prediction is achieved on a database of over 50.000 sounds and over 1600 concepts.