Madeleine Bonnet

LMS coupled adaptive prediction and system identification: a statistical model and transient mean analysis

The LMS algorithm has been successfully used in many system identification problems. However, when the input data covariance matrix is ill-conditioned, the algorithm converges slowly. To overcome the slow convergence, an adaptive structure is studied, which incorporates an LMS adaptive predictor (prewhitener) prior to the LMS algorithm for system identification (canceler). Since the prewhitener is also adaptive, the input to the LMS canceler is nonstationary, even when the input is stationary. Because of the coupling and the nonstationarity of LMS canceler input, analysis of the performance of the two adaptations is extremely difficult. A simple theoretical model of the coupled adaptations is presented and analyzed. First and second moment analysis indicates that the adaptive predictor significantly speeds up the LMS canceler as compared to a system without prewhitening and enlarges the stability domain of the canceler (larger allowable /spl mu/). Monte-Carlo simulations are presented which are in good agreement with the predictions of the mathematical model. >

Audio Watermarking and Fingerprinting: For Which Applications?

Although not a new issue, music piracy has acquired a new status in the digital era, as recordings can be easily copied and distributed. Watermarking has been proposed as a solution to this problem. It consists in embedding into the audio signal an inaudible mark containing copyright information. A different approach, called fingerprinting, consists in extracting a “fingerprint” from the audio signal. In association with a database, this fingerprint can be used to identify a recording, which is useful, for example, to monitor audio excerpts played by broadcasters and webcasters. There are far more applications to watermarking and fingerprinting. After a brief technical review, this article describes potential applications of both methodologies, showing which one is more suitable for each application.

Saturation effects in LMS adaptive echo cancellation for binary data

The effect of a saturation-type error nonlinearity in the weight update equation in least mean square (LMS) adaptive echo cancellation is investigated for an independent binary data model. A nonlinear difference equation is derived for the mean norm of the difference between the estimate and the unknown filter to be estimated by the algorithm. The difference equation is evaluated numerically. It is shown that far-end binary data interference is much more deleterious to algorithm transient behavior than far-end Gaussian data interference. The number of additional bits for the same cancellation convergence rates for binary versus Gaussian interference of the same power is studied as a function of various system parameters. Algorithm convergence rates are studied as a function of an arbitrary probability density function (PDF) for the far-end data. It is shown that a binary PDF causes the worst degradation and a Gaussian-shaped PDF causes the least degradation. >

Coupled adaptive prediction and system identification: a statistical model and transient analysis

A significant drawback of the least mean square (LMS) algorithm is slow convergence speed when the input covariance matrix is ill-conditioned. Two structures are presented and studied for increasing the convergence speed for this case. The structures incorporate a prewhitening filter prior to the usual LMS adaptation. When the prewhitening filter is also adaptive the input to the LMS algorithm is nonstationary. An analysis of the coupling effect between the two adaptive algorithms show that the adaptive prewhitener has the capability of significantly speeding up to LMS adaptation as compared to a system without prewhitening. When the prewhitening filter is fixed (nonadaptive), the structure is shown to be equivalent to the filtered-X LMS algorithm. Stability conditions and transient means behavior are given in the time domain, in terms of the parameters of the pre-whitening filter.<<ETX>>

Theoretical analysis of the ADPCM CCITT algorithm

The unfavorable effects of narrowband inputs on the decoder adjustment when the LMS algorithm is used are analyzed. An explanation is given for the behavior of the CCITT algorithm. Suboptimality of prediction is granted to achieve adjustment, resulting in a satisfactory tradeoff between reduction rate and adjustment. The link between adjustment and uniform stability is enhanced. >

A new QRD-based block adaptive algorithm

In this paper we present a new robust adaptive algorithm. It is derived from the standard QR decomposition based RLS (QRD-RLS) algorithm by introducing a non-orthogonal transform into the update recursion. Instead of updating an upper triangular matrix, as it is the case for the QRD-RLS, we adapt an upper triangular block diagonal matrix. The complexity of the algorithm, thus obtained, varies from O(N/sup 2/) to O(N) when the size of the diagonal blocks decreases. Simulations of the new algorithm have shown a better robustness than the standard QRD-based algorithm in the context of multichannel adaptive filtering with highly inter-correlated channels.

Mistracking in successive PCM/ADPCM transcoders

An analysis is presented of the causes of the accumulation of quantizing noise found in the transient state for successive CCITT adaptive differential pulse-code-modulation (ADPCM) transcoders connected synchronously. By decoupling the predictor and quantizer effect it is proved that, owing to a self-stabilization phenomenon, narrowband inputs cause local instabilities in the predictor of the jointly adaptive autoregressive moving-average-prediction/quantization used in the ADPCM 32-kb/s algorithm. Despite the assured global stability, these local instabilities are not synchronized at the encoder and its preceding decoder, and a mistracking occurs which creates quantizing noise accumulation. The tracking is then shown to be very sensitive to predictor/quantizer interaction. The discontinuities introduced in the standardized adaptive quantizer extend the mistracking problem to wideband inputs. A smoothed quantizer with reduced inauspicious interaction is proposed to remedy the problem. >

Digital prediction with spectral noise shaping

In order to avoid the coding noise accumulation in successive transcoders, a new fully digital structure is used where all the filters have quantized inputs. Whereas the coding noise spectrum is flat with the CCITT algorithm, with this new structure it presents a spectral shaping which can be further improved in high-frequency region, by filtering the quantizing noise.<<ETX>>