Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method

Orcioni, Simone; Terenzi, Alessandro; Cecchi, Stefania; Piazza, Francesco; Carini, Alberto

doi:10.17743/jaes.2018.0046

Cited by 21 publications

(20 citation statements)

References 22 publications

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“…As an example, a Wiener model [8,11] emulates the circuit as a linear filter followed by a static nonlinearity. Other black-box modelling methods include Volterra series models [13,14] and dynamical convolution [15]. Approaches which fall somewhere in between white and black box are known as "grey-box" models.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Guitar Amplifier Emulation with Deep Learning

Wright

Damskägg²,

Juvela³

et al. 2020

Applied Sciences

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This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.

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Section: Introductionmentioning

confidence: 99%

Real-Time Guitar Amplifier Emulation with Deep Learning

Wright

Damskägg²,

Juvela³

et al. 2020

Applied Sciences

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“…The diagonal number is the maximum time difference between the samples involved in each basis function. The limitation of the diagonal number finds justification in the experimental observation that in real-world nonlinear systems the "energy" of the nonlinear kernels tends to concentrate around the main diagonals, as was observed in nonlinear acoustic echo cancellation [13,15,49], nonlinear active noise control [50,51], identification of nonlinear systems [52,39]. For example, a WN filter of order 3, memory N , and diagonal numbers D 2 and D 3 for the second and third order basis functions, respectively, has the following diagonal form,…”

Section: The Wiener Basis Functionsmentioning

confidence: 99%

“…They realize a perfect orthogonality of the basis functions on a finite period and they have a finite maximum amplitude. Using PPSs, the kernel diagonal points can be accurately estimated without the need to resort to specific algorithms, as in [4,5,39]. Using PPSs, it is also possible to easily estimate the most relevant basis functions, according to some information criterion [32].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear system identification using Wiener basis functions and multiple-variance perfect sequences

et al. 2019

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The paper addresses nonlinear identification using the Wiener series. Differently from the traditional approach, the truncated Wiener series is expressed as a linear combination of basis functions, which are orthogonal for white Gaussian inputs. The coefficients of the basis functions are efficiently estimated with the cross-correlation method, computing the cross-correlation between the basis functions and the system output. Perfect periodic sequences (PPSs), which are periodic sequences guaranteeing the perfect orthogonality of the basis functions over a period, are also developed. The PPSs allow to avoid the estimation problems experienced with the cross-correlation method using stochastic inputs. The Wiener series formulation in terms of basis functions allows also to develop a novel, more efficient, multiple-variance identification method. Multiple-variance methods exploit input signals with multiple variances for estimating the Volterra kernels. They overcome the problem of locality of the solution, i.e., the fact that the identified model well approximates the nonlinear system only for input signal variances close to that used for the identification. Optimal values of the multiple variances are also studied in the paper. Experimental results, involving the identifications of real devices, show that the

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“…Speaking of black box approaches, these are the most widely adopted since they do not require any previous knowledge of the system. Some of the most wellknown models, such as the Volterra filters [18], belong to the category of black box approaches. These filters are derived from the Volterra series, to which a truncation is applied, because of the infinite number of terms.…”

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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Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method

Cited by 21 publications

References 22 publications

Real-Time Guitar Amplifier Emulation with Deep Learning

Real-Time Guitar Amplifier Emulation with Deep Learning

Nonlinear system identification using Wiener basis functions and multiple-variance perfect sequences

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

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