Echo cancellation and applications
TL;DR: Practical echo cancellation techniques, in particular, those used in telecommunications, are reviewed and current international standardization activities are discussed, and echo canceler implementation considerations are set forth.
Abstract: Practical echo cancellation techniques, in particular, those used in telecommunications, are reviewed. The various situations in which echoes are generated are examined. Echo path modeling techniques and adaptive algorithms for coefficient control are reviewed. Current international standardization activities are discussed, and echo canceler implementation considerations are set forth. These include echo cancelers for telephone circuits, echo cancelers for full-duplex data transmission over voice channels, acoustic echo cancelers, and echo cancelers for ISDN digital loop transmission. >
Citations
More filters
TL;DR: It is shown that the recursive least squares (RLS) algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise, and the TLS solution is seen to produce unbiased solutions.
Abstract: An algorithm for recursively computing the total least squares (TLS) solution to the adaptive filtering problem is described. This algorithm requires O(N) multiplications per iteration to effectively track the N-dimensional eigenvector associated with the minimum eigenvalue of an augmented sample covariance matrix. It is shown that the recursive least squares (RLS) algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise. The TLS solution on the other hand, is seen to produce unbiased solutions. Examples of standard adaptive filtering applications that result in noise being added to the adaptive filter input vector are cited. Computer simulations comparing the relative performance of RLS and recursive TLS are described. >
162 citations
Cites methods from "Echo cancellation and applications"
...T used in numerous adaptive signal processing applications including equalization of communications channels [ 11-[4], system identification [5]-[7], spectral estimation [8]-[ 1 11, and echo cancellation [I I]-[ 16 ]....
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TL;DR: In this article, a system and method for identifying the phoneme sound types that are contained within an audio speech signal is disclosed, which includes a microphone (12) and associated conditioning circuitry (14, 15, 16, 17, 18).
Abstract: A system and method for identifying the phoneme sound types that are contained within an audio speech signal is disclosed. The system includes a microphone (12) and associated conditioning circuitry (14), for receiving an audio speech signal and converting it to a representative electrical signal. The electrical signal is then sampled and converted to a digital audio signal with a digital-to-analog converter (34). The digital audio signal is input to a programmable digital sound processor (18), which digitally processes the sound so as to extract various time domain and frequency domain sound characteristics. These characteristics are input to a programmable host sound processor (20) which compares the sound characteristics to standard sound data. Based on this comparison, the host sound processor (20) identifies the specific phoneme sounds that are contained within the audio speech signal. The programmable host sound processor (20) further includes linguistic processing program methods to convert the phoneme sounds into English words or other natural language words. These words are input to a host processor (22), which then utilizes the words as either data or commands.
146 citations
Book•
01 Jan 2000
TL;DR: The author walks readers through a variety of advanced topics, clearly demonstrating how even such areas as spectral analysis, adaptive and nonlinear filtering, or communications and speech signal processing can be made readily accessible through clear presentations and a practical hands-on approach.
Abstract: From the Publisher:
Get a working knowledge of digital signal processing for computer science applications
The field of digital signal processing (DSP) is rapidly exploding, yet most books on the subject do not reflect the real world of algorithm development, coding for applications, and software engineering. This important new work fills the gap in the field, providing computer professionals with a comprehensive introduction to those aspects of DSP essential for working on todays cutting-edge applications in speech compression and recognition and modem design. The author walks readers through a variety of advanced topics, clearly demonstrating how even such areas as spectral analysis, adaptive and nonlinear filtering, or communications and speech signal processing can be made readily accessible through clear presentations and a practical hands-on approach. In a light, reader-friendly style, Digital Signal Processing: A Computer Science Perspective provides:
*A unified treatment of the theory and practice of DSP at a level sufficient for exploring the contemporary professional literature
*Thorough coverage of the fundamental algorithms and structures needed for designing and coding DSP applications in a high level language
*Detailed explanations of the principles of digital signal processors that will allow readers to investigate assembly languages of specific processors
*A review of special algorithms used in several important areas of DSP, including speech compression/recognition and digital communications
*More than 200 illustrations as well as an appendix containing the essential mathematical background
143 citations
TL;DR: The author gives an overview of progress made in the evolution of technology to provide DS1 rate telephone access in a restricted segment of the loop plant without intermediate repeaters, loop conditioning, or pair selection in assignment.
Abstract: The author gives an overview of progress made in the evolution of technology to provide DS1 rate telephone access in a restricted segment of the loop plant without intermediate repeaters, loop conditioning, or pair selection in assignment. This technology is called the high bit rate digital subscriber line (HDSL). Discussed are background information on electronics in the loop plant and characterization of the tranmission environment in the relevant frequency band. The progress of HDSL study project of the American National Standards Institute (ANSI) is outlined. Analytical and theoretical studies to determine the limits on the transmission capabilities of the loop plant, motivated by the need to determine the feasibility limits of HDSLs, are reviewed. Also discussed is progress in technical work on suitable transmission formats. The possibility of an asymmetrical digital subscriber line (ADSL), transmitting at the DS1 rate from the central office to a remote distribution point, through the entire nonloaded loop plant is discussed. >
84 citations
Patent•
20 Feb 1996TL;DR: In this paper, an echo canceling loudspeaker telephone includes a loudspeaker which produces a sound pressure wave in response to an input signal which is applied to an audio input thereof, and an echo filter is responsive to the input signal and generates an estimated echo signal.
Abstract: An echo canceling loudspeaker telephone includes a loudspeaker which produces a sound pressure wave in response to an input signal which is applied to an audio input thereof. This sound pressure wave includes a desired linear component which is a linear function of the input signal, and an undesired non-linear component which is a non-linear function of the input signal, and the sound pressure wave is transmitted along an acoustic path. A microphone is positioned in the acoustic path and converts the sound pressure wave into an output signal. An echo filter is responsive to the input signal and generates an estimated echo signal. This echo filter includes a loudspeaker model which generates an estimate of the sound pressure wave including an estimate of the linear component and an estimate of the non-linear component. This echo filter also includes an acoustic path model which generates an estimate of the acoustic path from the loudspeaker to the microphone. In addition, a subtractor subtracts the estimated echo signal from the output signal thereby reducing an echo portion of said sound signal.
81 citations
References
More filters
TL;DR: In this article, a method for making successive experiments at levels x1, x2, ··· in such a way that xn will tend to θ in probability is presented.
Abstract: Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function of x but is unknown to the experimenter, and it is desired to find the solution x = θ of the equation M(x) = α, where a is a given constant. We give a method for making successive experiments at levels x1, x2, ··· in such a way that xn will tend to θ in probability.
9,312 citations
01 Aug 1976
TL;DR: It is shown that for stationary inputs the LMS adaptive algorithm, based on the method of steepest descent, approaches the theoretical limit of efficiency in terms of misadjustment and speed of adaptation when the eigenvalues of the input correlation matrix are equal or close in value.
Abstract: This paper describes the performance characteristics of the LMS adaptive filter, a digital filter composed of a tapped delay line and adjustable weights, whose impulse response is controlled by an adaptive algorithm. For stationary stochastic inputs, the mean-square error, the difference between the filter output and an externally supplied input called the "desired response," is a quadratic function of the weights, a paraboloid with a single fixed minimum point that can be sought by gradient techniques. The gradient estimation process is shown to introduce noise into the weight vector that is proportional to the speed of adaptation and number of weights. The effect of this noise is expressed in terms of a dimensionless quantity "misadjustment" that is a measure of the deviation from optimal Wiener performance. Analysis of a simple nonstationary case, in which the minimum point of the error surface is moving according to an assumed first-order Markov process, shows that an additional contribution to misadjustment arises from "lag" of the adaptive process in tracking the moving minimum point. This contribution, which is additive, is proportional to the number of weights but inversely proportional to the speed of adaptation. The sum of the misadjustments can be minimized by choosing the speed of adaptation to make equal the two contributions. It is further shown, in Appendix A, that for stationary inputs the LMS adaptive algorithm, based on the method of steepest descent, approaches the theoretical limit of efficiency in terms of misadjustment and speed of adaptation when the eigenvalues of the input correlation matrix are equal or close in value. When the eigenvalues are highly disparate (λ max /λ min > 10), an algorithm similar to LMS but based on Newton's method would approach this theoretical limit very closely.
1,423 citations
TL;DR: It is shown how a deterministic differential equation can be associated with the algorithm and examples of applications of the results to problems in identification and adaptive control.
Abstract: Recursive algorithms where random observations enter are studied in a fairly general framework. An important feature is that the observations my depend on previous "outputs" of the algorithm. The considered class of algorithms contains, e.g., stochastic approximation algorithm, recursive identification algorithm, and algorithms for adaptive control of linear systems. It is shown how a deterministic differential equation can be associated with the algorithm. Problems like convergence with probability one, possible convergence points and asymptotic behavior of the algorithm can all be studied in terms of this differential equation. Theorems stating the precise relationships between the differential equation and the algorithm are given as well as examples of applications of the results to problems in identification and adaptive control.
1,370 citations
TL;DR: In this article, an overview of several methods, filter structures, and recursive algorithms used in adaptive infinite-impulse response (IIR) filtering is presented, and several important issues associated with adaptive IIR filtering, including stability monitoring, the SPR condition, and convergence are addressed.
Abstract: An overview is presented of several methods, filter structures, and recursive algorithms used in adaptive infinite-impulse response (IIR) filtering. Both the equation-error and output-error formulations are described, although the focus is on the adaptive algorithms and properties of the output-error configuration. These parameter-update algorithms have the same generic form, and they are based on a prediction-error performance criterion. A direct-form implementation of the adaptive filters is emphasized, but alternative realizations such as the parallel and lattice forms are briefly discussed. Several important issues associated with adaptive IIR filtering, including stability monitoring, the SPR condition, and convergence, are addressed. >
644 citations
01 Aug 1982
TL;DR: This paper presents a tutorial review of lattice structures and their use for adaptive prediction of time series, and it is shown that many of the currently used lattice methods are actually approximations to the stationary least squares solution.
Abstract: This paper presents a tutorial review of lattice structures and their use for adaptive prediction of time series Lattice filters associated with stationary covariance sequences and their properties are discussed The least squares prediction problem is defined for the given data case, and it is shown that many of the currently used lattice methods are actually approximations to the stationary least squares solution The recently developed class of adaptive least squares lattice algorithms are described in detail, both in their unnormalized and normalized forms The performance of the adaptive least squares lattice algorithm is compared to that of some gradient adaptive methods Lattice forms for ARMA processes, for joint process estimation, and for the sliding-window covariance case are presented The use of lattice structures for efficient factorization of covariance matrices and solution of Toeplitz sets of equations is briefly discussed
536 citations