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Toward blind removal of unwanted sound from orchestrated music

Toward blind removal of unwanted sound from orchestrated mus...

引用

Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Chang, SY Chun, J Univ Suwon Dept Elect Engn Kyungki do South Korea

ISBN: (纸本)0819437611

The problem addressed in this paper is to removing unwanted sounds from music sound. The sound to be removed could be disturbance such as cough. We shall present some preliminary results on this problem using statistical properties of signals. Our approach consists of three steps. We first estimate the fundamental frequencies and partials given noise-corrupted music sound. This gives us the autoregressive (AR) model of the music sound. Then se filter the noise-corrupted sound using the AR parameters. The filtered signal is then subtracted from the original noise-corrupted signal to get the disturbance. Finally the obtained disturbance is used as reference signal to eliminate the disturbance from the noise-corrupted music signal. Above three steps are carried out in a recursive manner using a sliding window or an infinitely growing window with an appropriate forgetting factor.

关键词： Wiener filter music signal processing interference cancellation

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Discrete time processing of linear scale invariant signals and systems

Discrete time processing of linear scale invariant signals a...

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Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Izzetoglu, M Onaral, B Chitrapu, P Bilgutay, N Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (纸本)0819437611

In this paper, we formulate a framework for discrete-time processing of Linear Scale Invariant (LSI) systems which are invariant to scale changes in time. Continuous-time LSI systems can be processed by Mellin and Modified Scale Transforms analogous to the use of the Laplace and Fourier Transforms in the continuous-time processing of LTI systems. In this work, we present the geometric sampling theorem to prevent aliasing in the scale domain. We also derive the perfect reconstruction filter in time domain, the discrete-time convolution sum, the Discrete Time Modified Scale Transform (DTMST) and the Discrete Modified Scale Transform (DMST) for geometrically sampled signals.

关键词： Transform theory Fourier transforms Convolution Electronic filtering signal processing Statistical analysis Toxic industrial chemicals Biomedical optics Computing systems Telecommunications

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Novel volterra predictor based on state-space equilibrium of nonlinear single- or multi-fractal signals

Novel volterra predictor based on state-space equilibrium of...

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Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Sabry-Rizk, M Zgallai, W City Univ London Dept Elect Elect & Informat Engn London EC1V 0HB England

ISBN: (纸本)0819437611

It has been quite common in the analysis of single- or multi-fractal signals originating from complex nonlinear systems to make a time-delayed construction of the state space attractor in which the dynamics can be qualitatively viewed. This involves the calculations of the embedding dimension and an appropriate time delay based on the signal nonlinear correlation behavioral pattern. This is usually followed by a sub-optimal short-term linear prediction in the signal time/subspace domain instead of the optimal nonlinear prediction in the time/frequency domain. In this paper, we propose to alleviate the sub-optimality problem and exploit the nonlinear signal dynamics embedded in the attractor and integrate them in the design of a new family of temporal multiple-step volterra predictors. Essentially, this is done by including relevant past information preserved in the signal up to a time t(d) = the embedded dimension X embedded time delay, and sampled at instances coincident with the embedded time delays, to predict one-step ahead adaptively, using the LMS criteria. The results obtained using several nonlinear (chaotic or non-chaotic) synthetic and measured biomedical signals and performing the novel quadratic- and cubic-volterra predictions show superior MSE performance, of as much as 30 dB, over those obtained using an optimized conventional one-step volterra predictor of the same order, particularly in the case of electrocardiogram signals. This is not achieved at the expense of any increase in the CPU time as the algorithm is designed to cater for new parallel volterra structures, with progressive delayed inputs.

关键词： state-space attractor nonlinear correlation multiple-step nonlinear quadratic- and cubic-volterra predictions

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Non-linear signal processing using index calculus DBNS arithmetic

Non-linear signal processing using index calculus DBNS arith...

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Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Muscedere, R Jullien, GA Dimitrov, vS Miller, WC Univ Windsor VLSI Res Grp Windsor ON N9B 3P4 Canada

ISBN: (纸本)0819437611

This paper discusses the use of a recently introduced index calculus Double-Base Number System (IDBNS) for representing and processing numbers for non-linear digital signal processing;the target application is a digital hearing aid processor. The IDBNS representation uses 2 orthogonal bases (2 and 3) to represent real numbers with arbitrary precision. By restricting the number of digits to one or two, it is possible to efficiently represent the real number using the indices of the bases rather than the distribution of the digits. In this paper we discuss the use of the two-digit form of this representation (2-IDBNS) to efficiently perform arithmetic associated with the non-linear processing required to correct the usual forms of hearing loss in a digital hearing aid. The non-linear processing takes the form of dynamic range compression as a function of frequency band. Currently developed digital hearing instrument processors require large dynamic range representations (20-24 bits) in order to accurately generate the dynamic range compression associated with typical hearing loss. We show that the natural non-linear representation afforded by the IDBNS provides both a more efficient signal representation and a more efficient technique for processing the dynamic range compression. We pay particular attention to a novel technique of converting from a linear binary input directly to the 2-IDBNS representation using an observation of partial cyclic repetition in the indices along with near unity approximants.

关键词： computer arithmetic non-linear number representation double-base number system hearing loss

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Color characterization for image indexing and machine vision

Color characterization for image indexing and machine vision

引用

Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Lin, SM Nair, D Natl Instruments Austin TX 78759 USA

ISBN: (纸本)0819437611

Color representation and comparison based on the histogram has proved to be very efficient for image indexing in content-based image retrieval and machine vision applications. However, the issues of color constancy and accurate color similarity measures remain unsolved. This paper presents a new algorithm for intensity-insensitive color characterization for image retrieval and machine vision applications. The color characterization algorithm divides the HSI (hue, saturation and intensity) color space into a given number of bins in such a way that the color characterization represents all the colors in the hue/saturation plane as well as black, white and gray colors. The color distribution in these bins of the HSI space is represented in the form of a one-dimensional vector called Color Spectrum vector (CSv). The color information that is stored in the CSv is insensitive to changes in the luminance. A weighted version of CSv called WCSv is introduced to take the similarity of the neighboring bins into account A Fuzzy Color Spectrum vector (FCSv) color representation vector that takes into account the human uncertainty in color classification process is also introduced here. The accuracy and speed of the algorithm is demonstrated in this paper through a series of experiments on image indexing and machine vision applications.

关键词： color comparison pattern matching clustering algorithm fuzzy set theory image indexing machine vision image processing

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On the regularized solution of block banded block Toeplitz systems

On the regularized solution of block banded block Toeplitz s...

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Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Bini, DA Farusi, A Fiorentino, G Meini, B Dipartimento Matemat I-56127 Pisa Italy

ISBN: (纸本)0819437611

Given two n. x n Toeplitz matrices T-1 and T-2, and a vector b epsilon R-n2, consider the linear system Ax = b - eta, where eta epsilon R-n2 is an unknown vector representing the noise and A = T-1 x T-2. Recovering approximations of a, given A and b, is encountered in image restoration problems. We propose a method for the approximation of the solution a: that has good regularization properties. The algorithm is based on a modified version of Newton's iteration for matrix inversion and relies on the concept of approximate displacement rank. We provide a formal description of the regularization properties of Newton's iteration in terms of filters and determine bounds to the number of iterations that guarantee regularization. The method is extended to deal with more general systems where A = Sigma (h)(i=1) T-1((i)) x T-2((i)). The cost of computing regularized inverses is O(n log n) operations (ops), the cost of solving the system Ax = b is O(n(2) log n) ops. Numerical experiments which show the effectiveness of our algorithm are presented.

关键词： Band Toeplitz systems Newton's iteration regularization displacement rank image processing

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Improved Radon Transform based method to quantify local stationarity

Improved Radon Transform based method to quantify local stat...

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Advance signal processing algorithms, Atchitectures, and implementations X

作者： Hedges, Robert A. Suter, Bruce W. Air Force Research Lab Rome United States

Establishing measures for local stationarity is an open problem in the field of time-frequency analysis. One promising theoretical measure, known as the spread, provides a means for quantifying potential correlation between signal elements. In this paper we investigate the issue of generalizing techniques developed by the authors to better estimate the spread of a signal. Existing techniques estimate the spread as the rectangular region of support of the associated expected ambiguity function oriented parallel to the axes. By applying Radon Transform techniques we can produce a parameterized model which describes the orientation of the region of support providing tighter estimates of the signal spread. Examples are provided that illustrate the enhancement of the new method.

关键词： signal processing

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Spectral generating theorem for nonstationary stochastic signals

Spectral generating theorem for nonstationary stochastic sig...

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Advance signal processing algorithms, Atchitectures, and implementations X

作者： Pitton, James W. Univ of Washington Seattle United States

This paper addresses time-frequency (TF) analysis from a statistical signal processing perspective, with the goal of developing a general statistical methodology for TF analysis. We review earlier work on statistical models for nonstationary stochastic signals, including frequency modulated locally stationary processes, which have covariance functions which yield nonnegative Wigner distributions. For such processes, time-frequency spectra may be defined without invoking `local-' or `quasi-' stationarity. These results are extended to include general time-varying linear systems and their associated time-frequency spectra. Any time-varying linear system driven by white noise has associated with it a nonnegative time-frequency spectrum. The bilinear class of time-frequency distributions are estimators of this time-frequency spectrum, as are adaptive methods such as positive time-frequency distributions and adaptive multitaper spectrograms. An analysis of the statistical properties of these estimators, including moments and distributional properties, is reviewed.

关键词： signal processing

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Efficient architecture for a multichannel array subbanding system with adaptive processing

Efficient architecture for a multichannel array subbanding s...

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Advance signal processing algorithms, Atchitectures, and implementations X

作者： Rabinkin, Daniel Nguyen, Huy MIT Lincoln Lab Lexington United States

An architecture is presented for front-end processing in a wideband array system which samples real signals. Such a system may be encountered in cellular telephony, radar, or low SNR digital communications receivers. The subbanding of data enables system data rate reduction, and creates a narrowband condition for adaptive processing within the subbands. The front-end performs passband filtering, equalization, subband decomposition and adaptive beamforming. The subbanding operation is efficiently implemented using a prototype lowpass finite impulse response (FIR) filter, decomposed into polyphase form, combined with a Fast Fourier Transform (FFT) block and a bank of modulating postmultipliers. If the system acquires real inputs, a single FFT may be used to operate on two channels, but a channel separation network is then required for recovery of individual channel data. A sequence of steps is described based on data transformation techniques that enables a maximally efficient implementation of the processing stages and eliminates the need for channel separation. Operation count is reduced, and several layers of processing are eliminated.

关键词： Adaptive filtering

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New matrix decomposition based on transforming the basis sets of the singular value decomposition yields principal features for time-frequency distributions

New matrix decomposition based on transforming the basis set...

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Advance signal processing algorithms, Atchitectures, and implementations X

作者： Groutage, Dale Bennink, David Naval Surface Warfare Cent Bremerton United States

We present a matrix decomposition that can be used to derive features from processes that are described by discrete-time, time-frequency representations. These include, among others, electrocardiograms, brain wave signals, seismic signals, vibration and shock signals, speech signals for voice recognition, and acoustic transient signals. The new decomposition is based on a transformation of the basis vectors of the singular value decomposition (SvD) which we call transformed singular value decomposition or TSvD. The transformed basis vectors are obtained by forming linear combinations of the original SvD basis vectors in a way such that the means of the transformed vectors are extrema of each other. The TSvD basis vectors are used to identify concentrations of energy density in the discrete-time, time-frequency representation by time and frequency descriptors. That is, descriptors such as the location in time, the spread in time, the location in frequency and the spread in frequency for each principal concentration of energy density can be obtained from the TSvD terms in the matrix decomposition series. Several examples are presented which illustrate the application of the new matrix decomposition for deriving principal time and frequency features from the discrete-time, time-frequency representations of nonstationary processes. Two of the examples illustrate how the derived time and frequency features can be used to classify individual short duration transient signals into respective classes, that is,: (1) automatically classify sonar signals as belonging to one of ten classes, and (2) automatically classify heartbeat signals as belonging to one of two people.

关键词： signal processing

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