A Fast Audio Clustering Using Vector Quantization and Second Order Statistics
Sound
2010-09-27 v1 Machine Learning
Abstract
This paper describes an effective unsupervised speaker indexing approach. We suggest a two stage algorithm to speed-up the state-of-the-art algorithm based on the Bayesian Information Criterion (BIC). In the first stage of the merging process a computationally cheap method based on the vector quantization (VQ) is used. Then in the second stage a more computational expensive technique based on the BIC is applied. In the speaker indexing task a turning parameter or a threshold is used. We suggest an on-line procedure to define the value of a turning parameter without using development data. The results are evaluated using 10 hours of audio data.
Cite
@article{arxiv.1009.4719,
title = {A Fast Audio Clustering Using Vector Quantization and Second Order Statistics},
author = {Konstantin Biatov},
journal= {arXiv preprint arXiv:1009.4719},
year = {2010}
}