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- Signal_subspace abstract "In signal processing, signal subspace methods are empirical linear methods for dimensionality reduction and noise reduction. These approaches have attracted significant interest and investigation recently in the context of speech enhancement, speech modeling, and speech classification research.Essentially the methods represent the application of a principal components analysis (PCA) approach to ensembles of observed time-series obtained by sampling, for example sampling an audio signal. Such samples can be viewed as vectors in a high-dimensional vector space over the real numbers. PCA is used to identify a set of orthogonal basis vectors (basis signals) which capture as much as possible of the energy in the ensemble of observed samples. The vector space spanned by the basis vectors identified by the analysis is then the signal subspace. The underlying assumption is that information in speech signals is almost completely contained in a small linear subspace of the overall space of possible sample vectors, whereas additive noise is typically distributed through the larger space isotropically (for example when it is white noise).By projecting a sample on a signal subspace, that is, keeping only the component of the sample that is in the signal subspace defined by linear combinations of the first few most energized basis vectors, and throwing away the rest of the sample, which is in the remainder of the space orthogonal to this subspace, a certain amount of noise filtering is then obtained.Signal subspace noise-reduction can be compared to Wiener filter methods. There are two main differences: The basis signals used in Wiener filtering are usually harmonic sine waves, into which a signal can be decomposed by Fourier transform. In contrast, the basis signals used to construct the signal subspace are identified empirically, and may for example be chirps, or particular characteristic shapes of transients after particular triggering events, rather than pure sinusoids. The Wiener filter grades smoothly between linear components that are dominated by signal, and linear components that are dominated by noise. The noise components are filtered out, but not quite completely; the signal components are retained, but not quite completely; and there is a transition zone which is partly accepted. In contrast, the signal subspace approach represents a sharp cut-off: an orthogonal component either lies within the signal subspace, in which case it is 100% accepted, or orthogonal to it, in which case it is 100% rejected. This reduction in dimensionality, abstracting the signal into a much shorter vector, can be a particularly desired feature of the method.In the simplest case signal subspace methods assume white noise, but extensions of the approach to colored noise removal and the evaluation of the subspace-based speech enhancement for robust speech recognition have also been reported.".
- Signal_subspace wikiPageExternalLink 45821&e=cta.
- Signal_subspace wikiPageID "11862679".
- Signal_subspace wikiPageLength "3620".
- Signal_subspace wikiPageOutDegree "20".
- Signal_subspace wikiPageRevisionID "432943412".
- Signal_subspace wikiPageWikiLink Additive_noise.
- Signal_subspace wikiPageWikiLink Additive_white_Gaussian_noise.
- Signal_subspace wikiPageWikiLink Basis_(linear_algebra).
- Signal_subspace wikiPageWikiLink Basis_vector.
- Signal_subspace wikiPageWikiLink Category:Noise_reduction.
- Signal_subspace wikiPageWikiLink Category:Signal_processing.
- Signal_subspace wikiPageWikiLink Chirp.
- Signal_subspace wikiPageWikiLink Dimension.
- Signal_subspace wikiPageWikiLink Dimension_reduction.
- Signal_subspace wikiPageWikiLink Dimensionality_reduction.
- Signal_subspace wikiPageWikiLink Fourier_transform.
- Signal_subspace wikiPageWikiLink Linear_subspace.
- Signal_subspace wikiPageWikiLink Noise_reduction.
- Signal_subspace wikiPageWikiLink Principal_component_analysis.
- Signal_subspace wikiPageWikiLink Principal_components_analysis.
- Signal_subspace wikiPageWikiLink Real_number.
- Signal_subspace wikiPageWikiLink Sampling_(signal_processing).
- Signal_subspace wikiPageWikiLink Signal_processing.
- Signal_subspace wikiPageWikiLink Sine_wave.
- Signal_subspace wikiPageWikiLink Sine_waves.
- Signal_subspace wikiPageWikiLink Smooth_function.
- Signal_subspace wikiPageWikiLink Smoothness.
- Signal_subspace wikiPageWikiLink Sound.
- Signal_subspace wikiPageWikiLink Vector_space.
- Signal_subspace wikiPageWikiLink White_noise.
- Signal_subspace wikiPageWikiLink Wiener_filter.
- Signal_subspace wikiPageWikiLinkText "Signal Subspace".
- Signal_subspace wikiPageWikiLinkText "Signal subspace".
- Signal_subspace wikiPageWikiLinkText "signal subspace".
- Signal_subspace hasPhotoCollection Signal_subspace.
- Signal_subspace wikiPageUsesTemplate Template:Cite_journal.
- Signal_subspace subject Category:Noise_reduction.
- Signal_subspace subject Category:Signal_processing.
- Signal_subspace hypernym Methods.
- Signal_subspace type Software.
- Signal_subspace type Field.
- Signal_subspace comment "In signal processing, signal subspace methods are empirical linear methods for dimensionality reduction and noise reduction. These approaches have attracted significant interest and investigation recently in the context of speech enhancement, speech modeling, and speech classification research.Essentially the methods represent the application of a principal components analysis (PCA) approach to ensembles of observed time-series obtained by sampling, for example sampling an audio signal.".
- Signal_subspace label "Signal subspace".
- Signal_subspace sameAs m.02rv_d8.
- Signal_subspace sameAs Q7512739.
- Signal_subspace sameAs Q7512739.
- Signal_subspace wasDerivedFrom Signal_subspace?oldid=432943412.
- Signal_subspace isPrimaryTopicOf Signal_subspace.