A blind segmentation approach to acoustic event detection based on i-vector
Zhen Huang, You-Chi Cheng, Kehuang Li, Ville Hautamäki, Chin‐Hui Lee · 2013
We propose a new blind segmentation approach to acous-tic event detection (AED) based on i-vectors. Conventional approaches to AED often required well-segmented data with non-overlapping boundaries for competing events. Inspired by block-based automatic image annotation in image retrieval tasks, we blindly segment audio streams into equal-length pieces, label the underlying observed acoustic events with mul-tiple categories and with no event boundary information, extract i-vector for them, and perform classification using support vec-tor machine and maximal figure-of-merit based classifiers. Ex-periments on various sets of audio data show promising results with an average of 8 % absolute gain in F1 over the conventional hidden Markov model based approach. An enhanced robustness at different noise levels is also observed. The key to the suc-cess lies in the enhanced discrimination power offered by the i-vector representation of the acoustic data. Index Terms: acoustic event detection, i-vector, blind segmen-tation, support vector machine, maximal figure-of-merit