In this paper we are dealing with audio segmentation. The audio tracks are sampled in short sequences which are classified into several classes. Every sequence can then be further analysed depending on the class it belongs to. We first describe simple techniques for segmentation in two or three classes. These methods rely on amplitude, spectral or cepstral analysis, and classical hidden markov models. From the limitations of these approaches, we propose a two level segmentation process. The segmentation is performed by computing several features for each audio sequence. These features are computed either on a complete audio segment or on a frame (set of samples) which is a subset of the audio segment. The proposed approach for microsegmentation of audio data consists of a combination of a K-Mean classifier at the segment level and of a Multidimensional Hidden Markov Model system using the frame decomposition of the signal. A first classification is obtained using the K-Mean classifier and segment-based features. Then final result comes from the use of Multidimensional Hidden Markov Models and frame-based features involving temporary results. Multidimensional Hidden Markov Models are an extension of classical Hidden Markov Models dedicated to multicomponent data. They are particularly adapted to our case where each audio segment can be characterized by several features of different natures. We illustrate our methods in the context of analysis of football audio tracks.
Final classification was done by using the Multidimensional Hidden Markov Models and frame-related features REF .
A two level strategy for audio segmentation
Digit. Signal Process.
Digit. Signal Process.
Computer Science