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Aug 2008

Volume 124, Issue 2, pp. 689-EL61

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Noise reduction combining time-frequency ε-filter and M-transform

Tomomi Abe, Mitsuharu Matsumoto, and Shuji Hashimoto

J. Acoust. Soc. Am. Volume 124, Issue 2, pp. 994-1005 (2008); (12 pages)

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This paper introduces noise reduction combining time-frequency ε-filter (TF ε-filter) and time-frequency M-transform (TF M-transform). Musical noise is an offensive noise generated due to noise reduction in the time-frequency domain such as spectral subtraction and TF ε-filter. It has a deleterious effect on speech recognition. To solve the problem, M-transform is introduced. M-transform is a linear transform based on M-sequence. The method combining the time-domain ε-filter (TD ε-filter) and time-domain M-transform (TD M-transform) can reduce not only white noise but also impulse noise. Musical noise is isolated in the time-frequency domain, which is similar to impulse noise in the time domain. On these prospects, this paper aims to reduce musical noise by improving M-transform for the time-frequency domain. Noise reduction by using TD M-transform and the TD ε-filter is first explained to clarify its features. Then, an improved method applying M-transform to the time-frequency domain, namely TF M-transform, is described. Noise reduction combining the TF ε-filter and TF M-transform is also proposed. The proposed method can reduce not only high-level nonstationary noise but also musical noise. Experimental results are also given to demonstrate the performance of the proposed method.
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43.60.Ac Theory of acoustic signal processing
43.60.Wy Non-stationary signal analysis, non-linear systems, and higher order statistics
43.60.Hj Time-frequency signal processing, wavelets

Time reversal of flexural waves in a beam at audible frequency

Dany Francoeur and Alain Berry

J. Acoust. Soc. Am. Volume 124, Issue 2, pp. 1006-1017 (2008); (12 pages) | Cited 2 times

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There has been very limited work on the application of time Reversal to the propagation of audible frequency waves in mechanical structures. The present work concentrates on the application of time reversal to the focusing of audible range, flexural waves in an infinite beam, and to the detection of local heterogeneity in such a beam. Practical applications of time reversal of flexural waves in structures include vibration energy focusing, detection of vibratory or acoustic sources, and detection of defects in mechanical structures. An analytical model of flexural wave propagation in the beam as well as sensing and emission using piezoelectric transducers is presented. Time reversal experiments are conducted and compared to the model results in either a homogeneous beam or a beam with point mass heterogeneities. In the various situations tested, it is shown that time reversal effectively compensates the spreading in time of the impulse due to the dispersive propagation of flexural waves. One interesting aspect of this property is the generation of large amplitude impulsive responses in the beam using remote actuators. Finally, the “Décomposition de l’Opérateur de Retournement Temporel” approach is examined to detect and localize point mass scatterers in the beam.
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43.60.Tj Wave front reconstruction, acoustic time-reversal, and phase conjugation
43.40.Cw Vibrations of strings, rods, and beams
43.60.Jn Source localization and parameter estimation

Prediction of the acoustic form function by neural network techniques for immersed tubes

A. Dariouchy, E. Aassif, G. Maze, D. Décultot, and A. Moudden

J. Acoust. Soc. Am. Volume 124, Issue 2, pp. 1018-1025 (2008); (8 pages)

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A new approach is used to predict the acoustic form function (FF) for an infinite length cylindrical shell excited perpendicularly to its axis using the artificial neural network (ANN) techniques. The Wigner–Ville distribution is used like a comparison tool between the FF calculated by the analytical method and that predicted by the ANN techniques for a stainless steel tube. During the development of the network, several configurations are evaluated for various radius ratios b/a (a: outer radius: b: inner radius of the tube). The optimal model is a network with one hidden layer. It is able to predict the FF with a mean relative error about 1.61% for the cases studied in this paper.
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43.60.Np Acoustic signal processing techniques for neural nets and learning systems
43.20.Fn Scattering of acoustic waves
43.60.Hj Time-frequency signal processing, wavelets
43.20.Ks Standing waves, resonance, normal modes

Removing additive noise via neuro-fuzzy-based reinforcement learning

Ching-Shun Lin and Chris Kyriakakis

J. Acoust. Soc. Am. Volume 124, Issue 2, pp. 1026-1037 (2008); (12 pages)

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In this paper, a systematic treatment for developing a noise removal system based on the fundamental principle of reinforcement learning and fuzzy cerebellar model articulation controller (FCMAC) is presented. The proposed system improves its performance over time through two mechanisms. First, the modified stochastic real-valued algorithm, learning from its own mistakes via the reinforcement signal and reinforcing its action to improve future performance, is used for searching the optimal noise spectrum for the overall training system. Second, system states associated with the positive reinforcement are memorized by FCMAC-based neurons, where, in the future, similar states will share the experiences already stored there and then lead the action to a more positive situation. In this work, FCMAC’s intrinsically poor approximation of rapidly varying functions is solved by taking the complex semicepstrum. In addition, the FCMAC provides an improvement in accuracy of function approximation without losing the property of generalization, which makes the high fidelity digital signal processing possible.
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43.60.Np Acoustic signal processing techniques for neural nets and learning systems
43.60.Lq Acoustic imaging, displays, pattern recognition, feature extraction
43.60.Ac Theory of acoustic signal processing
43.60.Cg Statistical properties of signals and noise

Adaptive spatial combining for passive time-reversed communications

João Gomes, António Silva, and Sérgio Jesus

J. Acoust. Soc. Am. Volume 124, Issue 2, pp. 1038-1053 (2008); (16 pages) | Cited 8 times

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Passive time reversal has aroused considerable interest in underwater communications as a computationally inexpensive means of mitigating the intersymbol interference introduced by the channel using a receiver array. In this paper the basic technique is extended by adaptively weighting sensor contributions to partially compensate for degraded focusing due to mismatch between the assumed and actual medium impulse responses. Two algorithms are proposed, one of which restores constructive interference between sensors, and the other one minimizes the output residual as in widely used equalization schemes. These are compared with plain time reversal and variants that employ postequalization and channel tracking. They are shown to improve the residual error and temporal stability of basic time reversal with very little added complexity. Results are presented for data collected in a passive time-reversal experiment that was conducted during the MREA’04 sea trial. In that experiment a single acoustic projector generated a 2/4-PSK (phase-shift keyed) stream at 200/400 baud, modulated at 3.6 kHz, and received at a range of about 2 km on a sparse vertical array with eight hydrophones. The data were found to exhibit significant Doppler scaling, and a resampling-based preprocessing method is also proposed here to compensate for that scaling.
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43.60.Dh Signal processing for communications: telephony and telemetry, sound pickup and reproduction, multimedia
43.60.Tj Wave front reconstruction, acoustic time-reversal, and phase conjugation
43.60.Gk Space-time signal processing, other than matched field processing
43.60.Fg Acoustic array systems and processing, beam-forming
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