Segmentation-by-classification system based on factor analysis

This paper proposes a novel audio segmentation-by-classification system based on Factor Analysis (FA) with a channel compensation matrix for each class and scoring the fixed-length segments as the log-likelihood ratio between class/no-class. The scores are smoothed and the most probable sequence is computed with a Viterbi algorithm. The system described here is designed to segment and classify the audio files coming from broadcast programs into five different classes: speech (SP), speech with noise (SN), speech with music (SM), music (MU) or others (OT). This task was proposed in the Albayzin 2010 evaluation campaign. The system is compared with the winning system of the evaluation achieving lower error rate in SP and SN. These classes represent 3/4 of the total amount of the data. Therefore, the FA segmentation system gets a reduction in the average segmentation error rate.