Publication

Investigations on modeling BRIR tails with filtered and coherence-matched noise

2009
Conference Papers

Abstract

This paper investigates to which extent the tails of left and right binaural room impulse responses (BRIRs) can be replaced by white Gaussian noise that has been processed to have the same energy decay relief and interaural coherence as the original BRIRs’ tail. For this purpose BRIRs were generated consisting of two parts where the first part is taken from the original BRIR and the second part is filtered and coherencematched noise. A sub jective test was carried out to investigate how the perceived similarity between original and modeled BRIRs decreases as the split point between the parts approaches the direct sound part of the BRIRs. Also, frequency-dependent and conventional frequency-independent interaural coherence matching were compared.

Official source

https://infoscience.epfl.ch/entities/publication/10edead7-44e0-40a7-8c07-2188505af09e

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2009
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/10edead7-44e0-40a7-8c07-2188505af09e

About this result

Related concepts (16)

Additive white Gaussian noise

Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics: Additive because it is added to any noise that might be intrinsic to the information system. White refers to the idea that it has uniform power spectral density across the frequency band for the information system. It is an analogy to the color white which may be realized by uniform emissions at all frequencies in the visible spectrum.

Gaussian noise

In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution). In other words, the values that the noise can take are Gaussian-distributed. The probability density function of a Gaussian random variable is given by: where represents the grey level, the mean grey value and its standard deviation.

Noise reduction

Noise reduction is the process of removing noise from a signal. Noise reduction techniques exist for audio and images. Noise reduction algorithms may distort the signal to some degree. Noise rejection is the ability of a circuit to isolate an undesired signal component from the desired signal component, as with common-mode rejection ratio. All signal processing devices, both analog and digital, have traits that make them susceptible to noise.

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