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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 01/2011
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Toon van Waterschoot
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ABSTRACT: This thesis deals with several open problems in acoustic echo cancellation and acoustic feedback control. Our main goal has been to develop solutions that provide a high performance and sound quality, and behave in a robust way in realistic conditions. This can be achieved by departing from the traditional ad-hoc methods, and instead deriving theoretically well-founded solutions, based on results from parameter estimation and system identification. In the development of these solutions, the computational efficiency has permanently been taken into account as a design constraint, in that the complexity increase compared to the state-of-the-art solutions should not exceed 50% of the original complexity.Full text available at http://hdl.handle.net/1979/2599.
The Journal of the Acoustical Society of America 12/2009; 126(6):3373. · 1.55 Impact Factor
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EURASIP J. Wireless Comm. and Networking. 01/2009; 2009.
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ABSTRACT: We present a blind coarse timing offset estimation technique for CP-OFDM and ZP-OFDM transmission over frequency selective channels. The technique exploits the cyclic prefix or zero-padding structure to estimate the starting position of the OFDM symbols without requiring any additional pilots. Simulation results are performed on various channel models with timing and frequency offsets. The presented technique is compared with the autocorrelation-based technique and various techniques in frequency selective channels. Our algorithm shows better performance results than those of the autocorrelation-based technique in NLOS channels, where the most predominant channel path is usually not the first arrival path.
EURASIP Journal on Wireless Communications and Networking. 01/2009;
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ABSTRACT: A cognitive radio system needs accurate knowledge of the radio spectrum it operates in. Blind modulation recognition techniques have been proposed to discriminate between single-carrier and multicarrier modulations and to estimate their parameters. Some powerful techniques use autocorrelation- and cyclic autocorrelation-based features of the transmitted signal applying to OFDM signals using a Cyclic Prefix time guard interval (CP-OFDM). In this paper, we propose a blind parameter estimation technique based on a power autocorrelation feature applying to OFDM signals using a Zero Padding time guard interval (ZP-OFDM) which in particular excludes the use of the autocorrelation- and cyclic autocorrelation-based techniques. The proposed technique leads to an efficient estimation of the symbol duration and zero padding duration in frequency selective channels, and is insensitive to receiver phase and frequency offsets. Simulation results are given for WiMAX and WiMedia signals using realistic Stanford University Interim (SUI) and Ultra-Wideband (UWB) IEEE 802.15.4a channel models, respectively.
EURASIP Journal on Wireless Communications and Networking. 01/2009;
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ABSTRACT: Acoustic feedback occurs in many audio applications involving musical sound signals. However, research efforts in acoustic feedback control have mainly been focused on speech applications. Since sound quality is of prime importance in audio applications, a proactive approach to acoustic feedback control is preferred to avoid ringing, howling, and excessive reverberation. Adaptive feedback cancellation (AFC) using a prediction-error-method (PEM)-based approach is a promising proactive solution, but existing algorithms are again designed for speech applications only. We propose to replace the all-pole near-end speech signal model in the PEM-based approach with a cascade of two near-end signal models: a tonal components model and a noise components model. We derive the identifiability conditions for joint identification of the acoustic feedback path and the cascaded near-end signal models. Depending on the model structure that is used for the near-end tonal components, three different PEM-based AFC algorithms are considered. By applying some relevant model approximations, the computational overhead of the proposed algorithms compared to the normalized least mean squares (NLMS) algorithm can be reduced to 25% of the NLMS complexity. Simulation results for both room acoustic and hearing aid scenarios indicate a significant performance improvement in terms of the misadjustment and the maximum stable gain increase.
Signal Processing. 01/2009;
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ABSTRACT: While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole) LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.
EURASIP Journal on Audio, Speech, and Music Processing. 01/2009;
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ABSTRACT: Engineering (OPTEC), the Belgian Programme on Interuniversity At-traction Poles, initiated by the Belgian Federal Science Policy Office IUAP P6/04 (DYSCO, 'Dynamical systems, control and optimization', 2007-2011), and EU/FP7-ICT-2007-1 Project 216785 ("Ultra-wide band real-time interference monitoring and cellular management strategies – UCELLS"), and was supported by the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen). The scientific responsibility is assumed by its authors. ABSTRACT Constrained pole-zero linear prediction (CPZLP) is proposed as a new method for parametric frequency estimation of multiple real sinusoids buried in noise. The method is based on a signal model that consists of a cascade of second-order constrained pole-zero models, thereby exploiting the linear prediction property of sinu-soidal signals. The signal model is parametrized directly with the unknown frequencies, which are then estimated using a numerical optimization approach. By independently optimizing each second-order stage in the cascade model, a computationally efficient algo-rithm is obtained with a complexity that is linear in both the data record length and the number of sinusoids. The linear complexity allows for using relatively long data records, leading to high ac-curacy even at low signal-to-noise ratios (SNR). Simulation results confirm that the CPZLP algorithm nearly achieves the Cramér-Rao lower bound for SNR as low as 5 dB.
09/2008;
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ABSTRACT: In many room acoustic signal processing applications, a room impulse response identification is needed to eliminate undesired effects such as echo, feedback, or reverberation. This is typically done using an adaptive filter driven by a speech or audio input signal. However, such signals exhibit poor excitation properties, which cause standard adaptive filtering algorithms to be very sensitive to disturbing signals, especially in the underdetermined case. A popular remedy is regularization, which is usually implemented with a scaled identity regularization matrix. This type of regularization is governed by a single regularization parameter, the value of which is often chosen in an arbitrary way. We propose to regularize the adaptive filter using a non-identity regularization matrix, in which prior knowledge on the unknown room impulse response may be incorporated. When knowledge of the disturbing signal is also used to add prefiltering and weighting in the adaptation, a new family of regularized adaptive filtering algorithms is obtained, which is shown to be optimal in a mean square error sense. Existing regularized algorithms can then be obtained as special cases, assuming limited or no prior knowledge is available. When combined with a recently proposed method of extracting prior knowledge from the acoustic setup, our algorithms exhibit superior convergence behaviour compared to existing algorithms in different simulation scenarios, while the additional computational cost is small.
Signal Processing. 01/2008;
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ABSTRACT: The performance of an acoustic echo canceller may be severely degraded by the presence of a near-end signal. In such a double-talk situation, the variance of the echo path estimate typically increases, resulting in slow convergence or even divergence of the adaptive filter. This problem is usually tackled by equipping the echo canceller with a double-talk detector that freezes adaptation during near-end activity. Nevertheless, there is a need for more robust adaptive algorithms since the adaptive filter's convergence may be affected considerably in the time interval needed to detect double-talk. Moreover, in some applications, near-end noise may be continuously present and then the use of a double-talk detector becomes futile. Robustness to double-talk may be established by taking into account the near-end signal characteristics, which are, however, unknown and time varying. In this paper, we show how concurrent estimation of the echo path and an autoregressive near-end signal model can be performed using prediction error (PE) identification techniques. We develop a general recursive prediction error (RPE) identification algorithm and compare it to three existing algorithms from adaptive feedback cancellation. The potential benefit of the algorithms in a double-talk situation is illustrated by means of computer simulations. It appears that especially in the stochastic gradient case a huge improvement in convergence behavior can be obtained
IEEE Transactions on Signal Processing 04/2007; · 2.63 Impact Factor
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INTERSPEECH 2007, 8th Annual Conference of the International Speech Communication Association, Antwerp, Belgium, August 27-31, 2007; 01/2007
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IEEE Transactions on Audio, Speech & Language Processing. 01/2007; 15:2561-2565.
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01/2006;
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IEEE Transactions on Signal Processing. 01/2006; 54:3426-3434.
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ABSTRACT: Recursive prediction error (RPE) identification algorithms are at-tractive alternatives to the traditional least-squares-based adaptive filtering algorithms for, e.g., room impulse response identification, in such applications as acoustic feedback and echo cancellation. It has however been observed that a recently proposed RPE algo-rithm suffers from numerical problems due to a scaling ambiguity in the calculation of the auxiliary variables. This problem is tack-led by regularizing the identification of some of the auxiliary vari-ables, which is called "dual regularization". This leads to a class of Dually Regularized Recursive Prediction Error (DR-RPE) iden-tification algorithms, with different choices of regularization meth-ods (Tikhonov or Levenberg-Marquardt) and matrices (possibly in-corporating prior knowledge). Simulation results confirm that the DR-RPE algorithms do not exhibit numerical problems, and as a consequence produce more accurate estimates of the room impulse response and of the auxiliary variables.
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ABSTRACT: This paper reports the experimental work performed in the framework of the ICT-1-216785 UCELLS project aiming at sensing ultra-wideband radio signals and their fingerprinting for cognitive radio management purposes. The experimental results demonstrate the successful capture and processing of UWB radio signals employing a photonic analog-to-digital converter. This approach permits both to cover a large frequency span without filtering and down-conversion stages altogether an excellent sensibility due to the engineering of the signal amplification stages through the optical and electronics domains. The experimental work herein reported demonstrates that -48 dBm peak levels can be sensed with 11.2 dB SNR using the proposed photonics technique. This technique enables the precise fingerprinting of the UWB transmitters in the area. Successful operation has been also demonstrated detecting the time frequency hopping of UWB transmissions captured and processed by the photonic analog-to-digital converter. The experimental results confirm the excellent performance of the fingerprinting algorithms identifying the presence of signal in each band of the frequency hopping and the silence periods. The identification of these transmission parameters enables cognitive management of the different transmitters maximizing the overall system capacity.
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ABSTRACT: In some acoustic echo cancellation scenarios, such as an automatic gain adjustment application, near-end noise may be continuously present. In this case a double-talk detector cannot be applied and the adaptive algorithm should behave in a robust way w.r.t. the dis-turbing near-end signal. From linear estimation theory it is known that the variance of the room impulse response estimate may be de-creased by taking into account the near-end signal characteristics. From the expression for the best linear unbiased estimate, we derive a prediction error criterion from which the near-end signal model and the room impulse response can be estimated concurrently. We propose a new recursive identification algorithm for minimization of the proposed prediction error criterion. The proposed algorithm is in fact a variant of a prediction error identification algorithm that was developed recently for adaptive feedback cancellation. Simula-tion results indicate that indeed a fast converging echo cancellation algorithm may be obtained with the proposed method, as compared to ordinary RLS and NLMS adaptive algorithms.
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ABSTRACT: Spectral monitoring has received considerable attention in the context of opportunistic and cognitive radio systems. The increasing number of wireless technologies calls for efficient techniques to monitor the radio frequency spectrum. Spectral monitoring is based on signal detection tools to reduce the spectral search to the signals of interest as well as estimation tools to identify their characteristics (carrier frequency, band-width, power, modulation, symbol duration...). In this paper, the spectral components are estimated by the averaged pe-riodogram non-parametric approach using an FFT. The sig-nals of interest are further processed to determine the type of modulation (single-carrier or multi-carrier). In particular, we develop a parameter estimation tool for ZP-OFDM signals based on power autocorrelation to determine their symbol and zero padding duration. Simulation results are provided for an extensive number of generated signals under frequency selective channels, to assess the performance of the signal detection in realistic scenarios.
