Computationally Efficient and Robust BIC-Based Speaker Segmentation

Efficient audio segmentation algorithms based on the BIC

A widely adopted algorithm for the audio segmentation is based on the Bayesian Information Criterion (BIC), applyed within a sliding variable-size analysis window. In this work, three different implementations of that algorithm are analyzed in detail: (i) one that keeps updated a pair of sums, that of input vectors and that of square input vectors, in order to save computations in estimating co...

Computationally efficient and robust frequency-domain GSC

In this paper, we propose a novel adaptive beamforming algorithm with low signal distortion capability and high computational efficiency. The proposed algorithm incorporates the crosstalk-resistant adaptive noise canceller (CTRANC) in place of the usual adaptive interference canceller (AIC). Doing so, a better signal leakage estimate is provided at the adaptive interference canceller input but ...

Speaker Diarization System Based on GMM and BIC

This paper presents an approach for speaker diarization based on a novel combination of Gaussian mixture model (GMM) and standard Bayesian information criterion (BIC). Gaussian mixture model provides a good description of feature vector distribution and BIC enables a proper merging and stopping criterion. Our system combines the advantage of these two method and yields favorable performance. Ex...

An adaptive BIC approach for robust audio stream segmentation

In this paper we focus on an audio segmentation. We present a novel method for robust estimation of decision-thresholds for accurate detection of acoustic change points in continuous audio streams. In standard segmentation procedures the decisionthresholds are usually set in advance and need to be tuned from development data. In the presented approach we tried to remove a need for using pre-det...

Robust Speaker segmentation and clustering for Meetings