UNSUPERVISED SIGNAL SEGMENTATION BASED ON TEMPORAL SPECTRAL CLUSTERING

ABSTRACT This paper presents an approach for applying spectral clustering to time series data. We define a novel similarity measure based on euclidean distance and temporal proximity between vectors. This metric is useful for conditioning matrices needed to perform spectral clustering, and its application leads to the detection of abrupt changes in a sequence of vectors. It defines a temporal segmentation of the signal. When the input to the algorithm is a speech signal, we further process the segments and achieve their labeling in one of three phonetic classes: silence, consonant or vowel. When the input signal is a video stream, the algorithm detects scene changes in the sequence of images. Our results are compared against classic unsupervised and supervised techniques, and evaluated with the phonetically labeled multi-language corpus OGI-MLTS and the video database of the french video indexing campaign ARGOS

Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia

International audienceThis paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach

Fusion de paramètres en classification Parole/Musique/Bruit

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Exploiting a Vowel Based Approach for Acted Emotion Recognition

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