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Nonlinear Audio Processing - SoundBridge
https://soundbridge.io/non-linear-audio-processing/#:~:text=One%20more%20example%20of%20nonlinearity%20in%20audio%20processing,production%20of%20so-called%20%E2%80%9Cphantom%20partials%E2%80%9D%20in%20piano%20tones.
Nonlinear Audio Processing - SoundBridge
https://soundbridge.io/non-linear-audio-processing/
Digital signal processing is mainly based on linear time-invariant systems. The assumption of linearity and time invariance is for sure valid for a great variety of technical systems. Especially for the systems where input and output signals are bounded to a specific amplitude range. To be precise, several analog audio process…
Nonlinear Audio Processing
http://www.dspguide.com/ch22/7.htm
Nonlinear Audio Processing. Digital filtering can improve audio signals in many ways. For instance, Wiener filtering can be used to separate frequencies that are mainly signal, from frequencies that are mainly noise (see Chapter 17). Likewise, deconvolution can compensate for an undesired convolution, such as in the restoration of old recordings (also discussed in …
(PDF) Nonlinear audio systems identification through …
https://www.academia.edu/70988234/Nonlinear_audio_systems_identification_through_audio_input_Gaussianization
Hence, audio nonlinear systems should be identified when they are excited by their real inputs (natural audio signals). But the properties of audio signals make them unsuitable for classical identification algorithms, since they are generally non-Gaussian, non …
Complex Nonlinearities for Audio Signal Processing
https://ccrma.stanford.edu/~jatin/papers/Complex_NLs.pdf
The most commonly used nonlinearity in audio signal processing is the saturating nonlinearity, where the input “clips” to a con- stant value as the input gain increases.
Reservoir Computing: a powerful Black-Box Framework for ...
https://www.dafx.de/paper-archive/2009/papers/paper_23.pdf
for nonlinear audio processing. Reservoir computing is a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It can in theory approximate arbitrary nonlinear dynamical systems with arbitrary precision, has an inherent temporal processing capability and is
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