Current Issue : January - March Volume : 2012 Issue Number : 1 Articles : 6 Articles
A fast, efficient and scalable algorithm is proposed, in this paper, for re-encoding of perceptually quantized wavelet-packet transform (WPT) coefficients of audio and high quality speech and is called \"adaptive variable degree-k zero-trees\" (AVDZ). The quantization process is carried out by taking into account some basic perceptual considerations, and achieves good subjective quality with low complexity. The performance of the proposed AVDZ algorithm is compared with two other zero-tree-based schemes comprising: 1- Embedded Zero-tree Wavelet (EZW) and 2- The set partitioning in hierarchical trees (SPIHT). Since EZW and SPIHT are designed for image compression, some modifications are incorporated in these schemes for their better matching to audio signals. It is shown that the proposed modifications can improve their performance by about 15-25%. Furthermore, it is concluded that the proposed AVDZ algorithm outperforms these modified versions in terms of both output average bit-rates and computation times....
An affine projection algorithm using regressive estimated error (APA-REE) is presented in this paper. By redefining the iterated error of the affine projection algorithm (APA), a new algorithm is obtained, and it improves the adaptive filtering convergence rate. We analyze the iterated error signal and the stability for the APA-REE algorithm. The steady-state weights of the APA-REE algorithm are proved to be unbiased and consist. The simulation results show that the proposed algorithm has a fast convergence rate compared with the APA algorithm....
Multiple built-in cameras and the small size of mobile phones are underexploited assets for creating novel applications that are ideal for pocket size devices, but may not make much sense with laptops. In this paper we present two vision-based methods for the control of mobile user interfaces based on motion tracking and recognition. In the first case the motion is extracted by estimating the movement of the device held in the user's hand. In the second it is produced from tracking the motion of the user's finger in front of the device. In both alternatives sequences of motion are classified using Hidden Markov Models. The results of the classification are filtered using a likelihood ratio and the velocity entropy to reject possibly incorrect sequences. Our hypothesis here is that incorrect measurements are characterised by a higher entropy value for their velocity histogram denoting more random movements by the user. We also show that using the same filtering criteria we can control unsupervised Maximum A Posteriori adaptation. Experiments conducted on a recognition task involving simple control gestures for mobile phones clearly demonstrate the potential usage of our approaches and may provide for ingredients for new user interface designs....
The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy....
Performing optimal bit-allocation with 3D wavelet coding methods is difficult, because energy is not conserved after applying the motion-compensated temporal filtering (MCTF) process and the spatial wavelet transform. The problem has been solved by extending the 2D wavelet coefficients weighting method with the consideration of the complicated pixel connectivity resulting from the lifting-based MCTF process. However, the computation of weighting factors demands much computing time and vast amount of memory. In this paper, we propose a method, which facilitates the property of sparse matrices, to compute the subband weighing factors efficiently. The proposed method is employed for the 5ââ?¬â??3 temporal filter and the 9ââ?¬â??7 temporal filter in the lifting-based MCTF process. Experiments on various video sequences show that the computing time and memory needed for the computation of weighting factors are dramatically reduced by applying the proposed approach....
Consider a sequence of an mth-order Autoregressive (AR) stationary discrete-time process and assume that at least m - 1 consecutive neighboring samples of an unknown sample are available. It is not important that the neighbors are from one side\r\nor are from both the left and right sides. In this paper, we find explicit solutions for the optimal linear estimation of the unknown sample in terms of the neighbors.We write the estimation errors as the linear combination of innovation noises.We also calculate\r\nthe corresponding mean square errors (MSE). To the best of our knowledge, there is no explicit solution for this problem. The known solutions are the implicit ones through orthogonality equations. Also, there are no explicit solutions when fewer than m-1\r\nsamples are available. The order of the process (m) and the feedback coefficients are assumed to be known....
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