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Projection of computer graphics images on walls or architecture, known as projection mapping, has been used widely and applied in many fields. In projection mapping, the relative alignment between the projected images and the target is important because the efficiency of projection mapping may be reduced due to misregistration. However, the process of alignment is manual and time-consuming. To solve this problem, we propose an automatic alignment method th at uses depth information and geometrical characteristics of a plane. This meth od enables the correction of a distorted image proj ected from an arbitrary angle. As a result, distortion is reduced by applying our proposed method, which is evaluated by the error rate defined. Performing additional experiments and evalua ting the effect of our method will be a part of our future works.

Several secondary path, acoustic noise cancellation modelling causes the problems to increase the complexity of ANC implementation, reduction of performance caused by modelling error and requirement of auxiliary noise for secondary path modelling. The acoustic noise generated is further compounded by using secondary path identification which makes the system complex. There are several available ANC algorithms that do not require secondary path estimation for modifying the FxLMS algorithms. Due to drawbacks such as slow convergence speed and, complexity of the phase shift mechanism, a novel approach with no secondary path modelling is adopted, in which the adaptation stability is guaranteed by switching the sign of the step size. It is combined with the online tuneable delay of the reference signal to significantly improve the adaptation convergence properties of the algorithm. A new mathematical modelling has been proposed to reduce the acoustic noise that increases the convergence criteria.

Speech source separation has been an important topic to realize speech-based human-machine interfaces or high quality hand-free communication with machines. For source separation, Independent Component Analysis (ICA) and time-frequency masking are powerful methods as a tool of Blind Source Separation (BSS) of speech mixtures. The latter method is based on the assumption called \W-Disjoint Orthogonality" which implies the cell component sparsity of speech in the time-frequency domain. One of the topics treated in this article is to introduce the time-frequency masking scheme is applied to the equilateral triangular array where the three delay estimates from each microphone pairs are obtained. In addition, it is used to improve histogram-mapping algorithm by integrate and coordinate transformation of three delay estimates. Some experiments in real environment for separating multiple sources are performed to verify the effectiveness.

This brief proposes a two-dimensional (2-D) Levinson algorithm and a lattice filter for the general case of the Autoregressive (AR) model with an asymmetric half-plane (AHP) support. The resulting Levinson algorithm and corresponding lattice filter solve the 2-D normal equation recursively, Although the 2-D signals of the model support are ordered into a one-dimensional (1-D) array, the ordering of the support signal is assigned voluntarily. The effects on the resulting model caused by different choices of support signal ordering are discussed. Finally, the validity of the proposed theory is confirmed through various simulations.

This paper presents a design method of the zero phase two-dimensional (2-D) finite impulse response (FIR) fan filters having rotated frequency response of a given prototype fan filter. The first step of our approach is to design a prototype filter with the impulse response whose domain is restricted within a circular region. This restriction prevents rotated impulse array to moving out from the rectangular region. Then, the rotation of prototype impulse response, which is given no more than on the rectgngular lattice grid, may cause the same rotation in the frequency domain. The second step is the interpolation of the impulse response on the new grids by the reconstruction algorithm of 2-D bandlimited discrete signals. Previous other methods for the rotation and the interpolation are compared with the proposed method. The obtained results for fan-type filter approximation by the proposed method show its validity.

We propose two methods for the disambiguation of results in time-delay based detection and localization of sound sources, when a triangle of microphones is applied for signal acquisition. A standard approach is to create histograms of time differences of arrival (TDOA) for each microphone pair in a triangular array and to create an averaged histogram. But each individual histogram is designed to detect unique orientation of source only within the local range of [−π/2, π/2]. Hence, taking the average for different pairs is not appropriate and such method suffers from ambiguity of results in the full range of orientations: [0, 2π]. Our first proposition is a delay vector transformation method, that combines corresponding delay measurements into vectors and transforms them into a 2-D space in which a full-range orientation histogram can finally be established and analyzed. In our second method, individual orientation histograms obtained for pairs of microphones are analyzed first and for each detected source two competitive hypotheses are created. Due to a final clustering of the hypothesis set a unique orientation of each source can be estimated.

We are developing two crucial improvements on the time-frequency masking approach to the blind speech separation of underdetermined mixtures when processing anechoic and echoic mixtures. First, the proposed method copes with the usually large amount of delay estimation error that appears in a low frequency band. This step generates a restrictive mask for phase delays on the basis of local and global energy distribution analysis. This mask allows the selected cells to contribute to the orientation histogram. Second, the strong WDO assumption (disjoint orthogonal frequency domain) is relaxed by allowing some frequency bins to be shared by both sources. By detecting fundamental frequencies of speakers at instantaneous time points, mask creation is supported by exploring their harmonic frequencies. The proposed method is proved to be effective and reliable in conducting experiments with both simulated and real-life mixtures. Santrauka Straipsnyje nagrinėjamas aklasis signalų šaltinių išskyrimas apdorojant signalų mišinius su aido efektu ar be jo. Detaliai pristatomi matematiškai bei eksperimentų su dirbtiniais ir realiais šnekos duomenimis rezultatais pagrindžiami du esminiai šio metodo patobulinimai. Pirmasis patobulinimas leidžia sumažinti vėlinimo žemuose dažniuose įtaką šnekos signalo išskyrimo klaidai. Antrasis patobulinimas, paremtas kalbėtojo pagrin­dinio dažnio sekimu, leidžia algoritmui išnaudoti tas pačias dažnių sritis skirtingiems signalų šaltiniams išskirti. Raktiniai žodžiai: šaltinių aklasis atskyrimas; histogramos klasterizavimas; spektrogramos analizė; kalbos rekonstravimas; maskavimas laiko ir dažnių skalėje

A proposed design method for optimal FIR filters enhances moving objects with a specific velocity vector from other moving objects and noise. A stochastic process approach is applied to the problem using a priori information of signals and the probability distribution of velocity vectors.

A method of system reduction using the continued fraction expansion about arbitrary points on the real frequency axis is proposed. It gives a good approximation and the procedure is suitable for digital computation.