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In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.

A new technique for speech enhancement is proposed, which is based on psychoacoustic criteria. The technique employs the auditory masking threshold, in order to extract information for the audible noise components. Those components are then removed using adaptive non-linear spectral modification. The main advantage of such an approach is that the speech signal is not affected by processing. In addition, very little information of the features of the noise is required. 11-3590-7803-0946-4/93 $3.00 0 1993 IEEE

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