9,803 research outputs found

    Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

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    For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home

    A New Under-Sampling Method to Face Class Overlap and Imbalance

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    Class overlap and class imbalance are two data complexities that challenge the design of effective classifiers in Pattern Recognition and Data Mining as they may cause a significant loss in performance. Several solutions have been proposed to face both data difficulties, but most of these approaches tackle each problem separately. In this paper, we propose a two-stage under-sampling technique that combines the DBSCAN clustering algorithm to remove noisy samples and clean the decision boundary with a minimum spanning tree algorithm to face the class imbalance, thus handling class overlap and imbalance simultaneously with the aim of improving the performance of classifiers. An extensive experimental study shows a significantly better behavior of the new algorithm as compared to 12 state-of-the-art under-sampling methods using three standard classification models (nearest neighbor rule, J48 decision tree, and support vector machine with a linear kernel) on both real-life and synthetic databases

    Training Noise-Robust Spoken Phrase Detectors with Scarce and Private Data: An Application to Classroom Observation Videos

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    We explore how to automatically detect specific phrases in audio from noisy, multi-speaker videos using deep neural networks. Specifically, we focus on classroom observation videos that contain a few adult teachers and several small children (\u3c 5 years old). At any point in these videos, multiple people may be talking, shouting, crying, or singing simultaneously. Our goal is to recognize polite speech phrases such as Good job , Thank you , Please , and You\u27re welcome , as the occurrence of such speech is one of the behavioral markers used in classroom observation coding via the Classroom Assessment Scoring System (CLASS) protocol. Commercial speech recognition services such as Google Cloud Speech are impractical because of data privacy concerns. Therefore, we train and test our own custom models using a combination of publicly available classroom videos from YouTube, as well as a private dataset of real classroom observation videos collected by our colleagues at the University of Virginia. We also crowdsource an additional 1152 recordings of polite speech phrases to augment our training dataset. Our contributions are the following: (1) we design a crowdsourcing task for efficiently labeling speech events in classroom videos, (2) we develop a neural network-based architecture for speech recognition, robust to noise and overlapping speech, and (3) we explore methods to synthesize new and authentic audio data, both to increase the training set size and reduce the class imbalance. Finally, using our trained polite speech detector, (4) we investigate the relationship between polite speech and CLASS scores and enable teachers to visualize their use of polite language
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