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Recognizing and localizing a recurring pattern is a problem with a variety of applications such as classification and localization of home appliances from their activation signals and estimating the relative alignment between records of a natural repetitive electrocardiography (ECG) signals in Bio-medical data. Most common approaches for recognizing a recurring pattern are generative and focus on...
This paper deals with the application of the convolutive version of dictionary learning to analyze in-situ audio recordings for bio-acoustics monitoring. We propose an efficient approach for learning and using a sparse convolutive model to represent a collection of spectrograms. In this approach, we identify repeated bioacoustics patterns, e.g., bird syllables, as words and represent new spectrograms...
In synthesis dictionary learning, data is compactly represented as sparse combination over a dictionary. In analysis dictionary learning, the dictionary directly transforms the data to produce a sparse outcome. In this paper, we concentrate on the problem of analysis dictionary learning under the weak supervision setting. We introduce a discriminative probabilistic model and present a novel approach...
Dictionary learning of spectrograms consists of detecting their fundamental spectra-temporal patterns and their associated activation signals. In this paper, we propose an efficient convolutive dictionary learning approach for analyzing repetitive bioacoustics patterns from a collection of audio recordings. Our method is inspired by the convolutive non-negative matrix factorization (CNMF) model. The...
We consider the problem of finding unknown patterns that are recurring across multiple sets. For example, finding multiple objects that are present in multiple images or a short DNA code that is repeated across multiple DNA sequences. Earlier work on the topic includes a statistical modeling approach in which the same template is placed at a random position in multiple independent sets. Using mixture...
We consider the problem of finding the same pattern in multiple sets. This problem can be applied in a variety of signal processing and machine learning problems including DNA sequencing and detection of electrical signatures. In our problem setting, each set contains only a single unknown pattern of interest among many other patterns. To understand the performance limitations associated with this...
We present an inference framework for automatic detection of activations of home appliances based on voltage envelope waveforms. We cast the problem of appliance detection and recognition as an inference problem. When the activation signatures are known, the problem reduces to a simple detection problem. When the activation signatures are unknown, the problem is reformulated as a blind joint delay...
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