Hammerstein Kernels Identification by Means of a Sine Sweep Technique Applied to Nonlinear Audio Devices Emulation

Schmitz, Thomas; Embrechts, Jean-Jacques

doi:10.17743/jaes.2017.0025

Cited by 14 publications

(17 citation statements)

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“…One can notice that the feature parameter is equal to one, so each sample x[n] has to be put in a list of one element. This is due to Python implementation where a list a=[a1,a2] has shape=[2,] but a list b= [[b1],[b2]] has shape= [2,1]. Note also that a vector containing the last num_step inputs (last buffer) have to be stored since the values [x[-1] ... x [-num_step]] are needed to compute the first values of the input tensor (see Fig.3).…”

Section: Emulation Of the Graphmentioning

confidence: 99%

“…In previous researches we have focused on Volterra series models [4,5] and more specially on its subclass, the Wiener-Hammerstein cascade models [6,7]. More specifically, researches on Hammerstein model have led to a fast Hammerstein Identification by Sine Sweep (HKISS) method [1,2]. However, this kind of model is not sufficiently complex to correctly perform the emulation of wide range of guitar signals [1].…”

Section: Introductionmentioning

confidence: 99%

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Real Time Emulation of Parametric Guitar Tube Amplifier with Long Short Term Memory Neural Network

Schmitz

Embrechts

2018

Computer Science &Amp; Information Technology

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Numerous audio systems for musicians are expensive and bulky. Therefore, it could be advantageous to model them and to replace them by computer emulation. In guitar players' world, audio systems could have a desirable nonlinear behavior (distortion effects). It is thus difficult to find a simple model to emulate them in real time. Volterra series model and its subclass are usual ways to model nonlinear systems. Unfortunately, these systems are difficult to identify in an analytic way. In this paper we propose to take advantage of the new progress made in neural networks to emulate them in real time. We show that an accurate emulation can be reached with less than 1% of root mean square error between the signal coming from a tube amplifier and the output of the neural network. Moreover, the research has been extended to model the Gain parameter of the amplifier.

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Section: Emulation Of the Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real Time Emulation of Parametric Guitar Tube Amplifier with Long Short Term Memory Neural Network

Schmitz

Embrechts

2018

Computer Science &Amp; Information Technology

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“…The first neural models for audio effects processing imitated time-invariant linear and nonlinear filtering [6][7][8]. Deep neural models are the newest phase in the black-box modeling of audio devices, which had previously relied on nonlinear system identification methods, such as Volterra series [9][10][11][12] or the Wiener-Hammerstein model [13]. The present paper focuses on neural modeling of time-variant effects, which requires a different approach and poses a challenge during training, as the target behavior varies over time.…”

Section: Introductionmentioning

confidence: 99%

Neural Modeling of Phaser and Flanging Effects

Wright

Välimäki

2021

J. Audio Eng. Soc.

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“…For the Hammerstein model, the non-linear static part is placed before the linear one [24], [25], [26], [27], [28], whereas for the Wiener model the opposite happens, i.e., the non-linear part follows the linear one [24], [26]. Finally, for the Wiener-Hammerstein model, two linear parts are present and placed before and after the non-linear one [24], [29].…”

Section: Introductionmentioning

confidence: 99%

A Swept-Sine-Type Single Measurement to Estimate Intermodulation Distortion in a Dynamic Range of Audio Signal Amplitudes

Burrascano

Terenzi

Cecchi

et al. 2021

IEEE Trans. Instrum. Meas.

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In the world of real audio systems it is extremely important to model and identify their non-linear behaviour, especially in the case of professional audio devices. In this context, it is useful to have a quantitative estimation of the non-linearity degree of the device, which can be obtained by exploiting an efficient and rapid measurement methodology. In this paper, we propose an original estimation technique targeting the third-order intermodulation distortion, and based on a single detection. The proposed technique can be implemented both on devices operating in baseband and in bandpass. Starting from the same single detection, the technique allows to give either an estimate of the third-order intermodulation distortion for the signal level actually used, and to extrapolate the estimate of the intermodulation distortion to signal levels different from the one actually used. Experimental verifications on real audio devices have allowed to validate the procedure in operational situations, thus confirming the validity of the proposed approach.

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Hammerstein Kernels Identification by Means of a Sine Sweep Technique Applied to Nonlinear Audio Devices Emulation

Cited by 14 publications

References 0 publications

Real Time Emulation of Parametric Guitar Tube Amplifier with Long Short Term Memory Neural Network

Real Time Emulation of Parametric Guitar Tube Amplifier with Long Short Term Memory Neural Network

Neural Modeling of Phaser and Flanging Effects

A Swept-Sine-Type Single Measurement to Estimate Intermodulation Distortion in a Dynamic Range of Audio Signal Amplitudes

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