System and method for audio classification based on unsupervised attribute learning

摘要

Described is an audio classification system for classifying audio signals. In operation, the system extracts salient patches from an intensity spectrogram of an audio signal. Thereafter, multi-scale global average pooling (GAP) features are extracted for all salient patches. The GAP features are clustered, with each cluster becoming a key attribute. A test audio signal can then be mapped onto a histogram of key attributes. Based on the histogram, the test audio signal can then be classified as a sound class, allowing for operation of a device based on the classification of the sound class.