An efficient speech enhancement algorithm based on spectral classification and Wiener filtering is proposed. Under the assumption that the location of the target source is known, the spectral classification in the fre...
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An efficient speech enhancement algorithm based on spectral classification and Wiener filtering is proposed. Under the assumption that the location of the target source is known, the spectral classification in the frequency domain is performed by the time delay and the magnitude ratio between dual microphone inputs. Then, the Wiener filter is applied to enhance noisy speech. Experimental results show that the proposed algorithm improves the speech quality and the recognition accuracy significantly.
A novel algorithm is presented for estimating the nominal angles and angular spreads of multiple coherently distributed sources in a uniform linear array (ULA). Based on a decoupled generalised array response vector a...
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A novel algorithm is presented for estimating the nominal angles and angular spreads of multiple coherently distributed sources in a uniform linear array (ULA). Based on a decoupled generalised array response vector and symmetric ULA, the proposed algorithm can estimate the nominal angles without any angular signal density model assumptions of the sources. The angular spread for each source is then estimated in one-dimensional (1-D) parameter space, and L multiple sources localised using L + 1 1-D searches instead of a 2-D search.
We describe the Berkeley wavelet transform (BWT), a two-dimensional triadic wavelet transform. The BWT comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and ...
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We describe the Berkeley wavelet transform (BWT), a two-dimensional triadic wavelet transform. The BWT comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and the other has even symmetry. By translation and scaling of the whole set (plus a single constant term), the wavelets form a complete, orthonormal basis in two dimensions. The BWT shares many characteristics with the receptive fields of neurons in mammalian primary visual cortex (VU Like these receptive fields, BWT wavelets are localized in space, tuned in spatial frequency and orientation, and form a set that is approximately scale invariant. The wavelets also have spatial frequency and orientation bandwidths that are comparable with biological values. Although the classical Gabor wavelet model is a more accurate description of the receptive fields of individual V1 neurons, the BWT has some interesting advantages. It is a complete, orthonormal basis and is therefore inexpensive to compute, manipulate, and invert. These properties make the BWT useful in situations where computational power or experimental data are limited, such as estimation of the spatiotemporal receptive fields of neurons.
Unlike traditional online games, metaverses must dynamically provide content-which is mostly user-generated and continually modified-to users depending on their location in the virtual world. To better understand this...
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Unlike traditional online games, metaverses must dynamically provide content-which is mostly user-generated and continually modified-to users depending on their location in the virtual world. To better understand this emerging type of application, the authors performed a detailed analysis of Second Life, a popular metaverse, and determined that its computational and communication requirements place significant demands on servers, clients, and the network. As virtual worlds evolve to support more users, interaction types, and realism, these demands will increase.
HLAMatchmaker is a computer algorithm that determines HLA compatibility at the structural level. Donor-recipient histocompatibility is assessed with polymorphic amino acid configurations that represent structurally de...
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HLAMatchmaker is a computer algorithm that determines HLA compatibility at the structural level. Donor-recipient histocompatibility is assessed with polymorphic amino acid configurations that represent structurally defined elements of HLA epitopes originally assigned as triplets and more recently as eplets. For many patients, HLAMatchmaker can identify mismatched HLA antigens that can be considered compatible at the structural level. Structurally based HLA matching reduces humoral allosensitization and correlates with good transplant outcome. Moreover, HLAMatchmaker is useful in the analysis of serum antibody reactivity and benefits the strategy of identifying acceptable mismatches for highly sensitized patients.
In cortical neural networks, connections from a given neuron are either inhibitory or excitatory but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no...
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In cortical neural networks, connections from a given neuron are either inhibitory or excitatory but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no general solution to the problem of converting such unrealistic network models into biologically plausible models that respect this constraint. We demonstrate a constructive transformation of models that solves this problem for both feedforward and dynamic recurrent networks. The resulting models give a close approximation to the original network functions and temporal dynamics of the system, and they are biologically plausible. More precisely, we identify a general form for the solution to this problem. As a result, we also describe how the precise solution for a given cortical network can be determined empirically.
We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boo...
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We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boosting algorithms are closely related to models of mislabeling in which the label is erroneously exchanged for others. For the two boosting algorithms, theoretical aspects supporting the robustness for mislabeling are explored. We apply the proposed two boosting methods for synthetic and real data sets to investigate the performance of these methods, focusing on robustness, and confirm the validity of the proposed methods.
Deep belief networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton, Osindero, and Teh (2006) along with a greedy layer-wise unsupervised learn...
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Deep belief networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton, Osindero, and Teh (2006) along with a greedy layer-wise unsupervised learning algorithm. The building block of a DBN is a probabilistic model called a restricted Boltzmann machine (RBM), used to represent one layer of the model. Restricted Boltzmann machines are interesting because inference is easy in them and because they have been successfully used as building blocks for training deeper models. We first prove that adding hidden units yields strictly improved modeling power, while a second theorem shows that RBMs are universal approximators of discrete distributions. We then study the question of whether DBNs with more layers are strictly more powerful in terms of representational power. This suggests a new and less greedy criterion for training RBMs Within DBNs.
Motivation: Cis-acting regulatory elements are frequently constrained by both sequence content and positioning relative to a functional site, such as a splice or polyadenylation site. We describe an approach to regula...
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Motivation: Cis-acting regulatory elements are frequently constrained by both sequence content and positioning relative to a functional site, such as a splice or polyadenylation site. We describe an approach to regulatory motif analysis based on non-negative matrix factorization (NMF). Whereas existing pattern recognition algorithms commonly focus primarily on sequence content, our method simultaneously characterizes both positioning and sequence content of putative motifs. Results: Tests on artificially generated sequences show that NMF can faithfully reproduce both positioning and content of test motifs. We show how the variation of the residual sum of squares can be used to give a robust estimate of the number of motifs or patterns in a sequence set. Our analysis distinguishes multiple motifs with significant overlap in sequence content and/or positioning. Finally, we demonstrate the use of the NMF approach through characterization of biologically interesting datasets. Specifically, an analysis of mRNA 3'-processing (cleavage and polyadenylation) sites from a broad range of higher eukaryotes reveals a conserved core pattern of three elements.
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