Distortion of Musical Signals by Means of Multiband Waveshaping

Fernandez-Cid, P.; Quirós, Javier Casajús

doi:10.1076/jnmr.30.3.279.7476

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…nonlinear function of a single order only and the branch input signals are lowpass filtered to assure that no aliasing can occur [8], [5]. A filterbank form was presented in [9]. Thornburg suggested that a nonlinear function could be approximated with a lower-order polynomial to reduce aliasing [1].…”

Section: Introductionmentioning

confidence: 99%

Antiderivative Antialiasing for Memoryless Nonlinearities

Bilbao

Esqueda

Parker

et al. 2017

IEEE Signal Process. Lett.

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Aliasing is a commonly-encountered problem in audio signal processing, particularly when memoryless nonlinearities are simulated in discrete time. A conventional remedy is to operate at an oversampled rate. A new aliasing reduction method is proposed here for discrete-time memoryless nonlinearities, which is suitable for operation at reduced oversampling rates. The method employs higher order antiderivatives of the nonlinear function used. The first order form of the new method is equivalent to a technique proposed recently by Parker et al. Higher order extensions offer considerable improvement over the first antiderivative method, in terms of the signal-to-noise ratio. The proposed methods can be implemented with fewer operations than oversampling and are applicable to discrete-time modeling of a wide range of nonlinear analog systems.

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Section: Introductionmentioning

confidence: 99%

Antiderivative Antialiasing for Memoryless Nonlinearities

Bilbao

Esqueda

Parker

et al. 2017

IEEE Signal Process. Lett.

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“…The brute force and effective remedy is oversampling, usually at four to eight times the audio rate [3,4,5]. Specialised techniques, suitable for memoryless nonlinearities of polynomial type [6,7], and for hard-clipped nonlinearities [8], have been proposed. A recent approach, suitable for memoryless nonlinearities of arbitrary type, is based on a very different idea: that of operation on the antiderivative of the nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Antiderivative antialiasing, lagrange interpolation and spectral flatness

Bilbao

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Välimäki

2017

2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

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Aliasing is major problem in any audio signal processing chain involving nonlinearity. The usual approach to antialiasing involves operation at an oversampled rate-usually 4 to 8 times an audio sample rate. Recently, a new approach to antialiasing in the case of memoryless nonlinearities has been proposed, which relies on operations over the antiderivative of the nonlinear function, and which allows for antialiasing at audio or near-audio rates, and without regard to the particular form of the nonlinearity (i.e., polynomial, or hard clipping). Such techniques may be deduced through an application of Lagrange interpolation over unequally-spaced values, and, furthermore, may be constrained to behave as spectrally transparent "throughs" for nonlinearities which reduce to linear at low signal amplitudes. Numerical results are presented.

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