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Energy Disaggregation via Discriminative Sparse Coding
"... Energy disaggregation is the task of taking a wholehome energy signal and separating it into its component appliances. Studies have shown that having devicelevel energy information can cause users to conserve significant amounts of energy, but current electricity meters only report wholehome data. ..."
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Energy disaggregation is the task of taking a wholehome energy signal and separating it into its component appliances. Studies have shown that having devicelevel energy information can cause users to conserve significant amounts of energy, but current electricity meters only report wholehome data. Thus, developing algorithmic methods for disaggregation presents a key technical challenge in the effort to maximize energy conservation. In this paper, we examine a large scale energy disaggregation task, and apply a novel extension of sparse coding to this problem. In particular, we develop a method, based upon structured prediction, for discriminatively training sparse coding algorithms specifically to maximize disaggregation performance. We show that this significantly improves the performance of sparse coding algorithms on the energy task and illustrate how these disaggregation results can provide useful information about energy usage. 1
Sparse Representations of Polyphonic Music
 SIGNAL PROCESSING
, 2005
"... We consider two approaches for sparse decomposition of polyphonic music: a timedomain approach based on shiftinvariant waveforms, and a frequencydomain approach based on phaseinvariant power spectra. When trained on an example of a MIDIcontrolled acoustic piano recording, both methods produce di ..."
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Cited by 20 (6 self)
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We consider two approaches for sparse decomposition of polyphonic music: a timedomain approach based on shiftinvariant waveforms, and a frequencydomain approach based on phaseinvariant power spectra. When trained on an example of a MIDIcontrolled acoustic piano recording, both methods produce dictionary vectors or sets of vectors which represent underlying notes, and produce component activations related to the original MIDI score. The timedomain method is more computationally expensive, but produces sampleaccurate spikelike activations and can be used for a direct timedomain reconstruction. The spectral domain method discards phase information, but is faster than the timedomain method and retains more higherfrequency harmonics. These results suggest that these two methods would provide a powerful yet complementary approach to automatic music transcription or objectbased coding of musical audio.
TRANSFORMATION INVARIANT SPARSE CODING
"... Sparse coding is a well established principle for unsupervised learning. Traditionally, features are extracted in sparse coding in specific locations, however, often we would prefer invariant representation. This paper introduces a general transformation invariant sparse coding (TISC) model. The mod ..."
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Sparse coding is a well established principle for unsupervised learning. Traditionally, features are extracted in sparse coding in specific locations, however, often we would prefer invariant representation. This paper introduces a general transformation invariant sparse coding (TISC) model. The model decomposes images into features invariant to location and general transformation by a set of specified operators as well as a sparse coding matrix indicating where and to what degree in the original image these features are present. The TISC model is in general overcomplete and we therefore invoke sparse coding to estimate its parameters. We demonstrate how the model can correctly identify components of nontrivial artificial as well as real image data. Thus, the model is capable of reducing feature redundancies in terms of prespecified transformations improving the component identification. 1.
1Efficient Algorithms for Convolutional Sparse Representations
"... Abstract—When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multivalued and not o ..."
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Abstract—When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multivalued and not optimised with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both singlevalued and jointly optimised over the entire image. While this form of sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimisation problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.