Current Issue : April - June Volume : 2016 Issue Number : 2 Articles : 4 Articles
This article proposes high-order balanced multi-band multiwavelet packet transforms for denoising remote sensing\nimages. First, properties of several wavelet transforms and their relationships are analyzed. The article then presents\ntheoretical principles and a fast algorithm for constructing high-order balanced multi-band multiwavelet packet\ntransforms. The remote sensing image denoising method based on this transform scheme is then described, and its\nutility is demonstrated by illustrative results of its application to denoise remote sensing images. The method provides\nclear improvements in denoising quality, due to the balanced order or band number, consistently outperforming\ntraditional wavelet transform-based methods in terms of both visual quality and evaluation indicators. The method also\nincurs reasonable computational costs compared with the traditional methods....
Timeââ?¬â??frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT),\nwavelet transform (WT) and their synchrosqueezed versions (SWFT, SWT), provide powerful analysis\ntools. Here we present a thorough review of these TFRs, summarizing all practically relevant aspects\nof their use, reconsidering some conventions and introducing new concepts and procedures to\nadvance their applicability and value. Furthermore, a detailed numerical and theoretical study of\nthree specific questions is provided, relevant to the application of these methods, namely: the\neffects of the window/wavelet parameters on the resultant TFR; the relative performance of different\napproaches for estimating parameters of the components present in the signal from its TFR; and the\nadvantages/drawbacks of synchrosqueezing. In particular, we show that the higher concentration of the\nsynchrosqueezed transforms does not seem to imply better resolution properties, so that the SWFT and\nSWT do not appear to provide any significant advantages over the original WFT and WT apart from\na more visually appealing pictures. The algorithms and Matlab codes used in this work, e.g. those for\ncalculating (S)WFT and (S)WT, are freely available for download....
In this paper, we propose a new microphone array signal processing technique, which increases the number of\nmicrophones virtually by generating extra signal channels from real microphone signals. Microphone array signal\nprocessing methods such as speech enhancement are effective for improving the quality of various speech\napplications such as speech recognition and voice communication systems. However, the performance of speech\nenhancement and other signal processing methods depends on the number of microphones. Thus, special\nequipment such as a multichannel A/D converter or a microphone array is needed to achieve high processing\nperformance. Therefore, our aim was to establish a technique for improving the performance of array signal processing\nwith a small number of microphones and, in particular, to increase the number of channels virtually by synthesizing\nvirtual microphone signals, or extra signal channels, from two channels of microphone signals. Each virtual microphone\nsignal is generated by interpolating a short-time Fourier transform (STFT) representation of the microphone signals.\nThe phase and amplitude of the signal are interpolated individually. The phase is linearly interpolated on the basis of a\nsound propagation model, and the amplitude is nonlinearly interpolated on the basis of �² divergence. We also\nperformed speech enhancement experiments using a maximum signal-to-noise ratio (SNR) beamformer equipped\nwith virtual microphones and evaluated the improvement in performance upon introducing virtual microphones....
Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To\nimplement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized\nfrequency-domain adaptive filtering (GFDAF) algorithm are required for satisfactory convergence behavior. In this\npaper, the GFDAF algorithm is rigorously derived as an approximation of the block recursive least-squares (RLS)\nalgorithm. Thereby, the original formulation of the GFDAF algorithm is generalized while avoiding an error that has\nbeen in the original derivation. The presented algorithm formulation is applied to pruned transform-domain\nloudspeaker-enclosure-microphone models in a mathematically consistent manner. Such pruned models have\nrecently been proposed to cope with the tremendous computational demands of massive multichannel AEC. Beyond\nits generalization, a regularization of the GFDAF is shown to have a close relation to the well-known block\nleast-mean-squares algorithm....
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