Towards a better balance in sinusoidal plus stochastic representation

Schijndel, Nicolle H. van; Gómez, M. A.; Heusdens, Richard

doi:10.1109/aspaa.2003.1285865

Cited by 6 publications

(3 citation statements)

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“…Furthermore, we compared PLPC with the perceptual transform based on the masking threshold of the total input signal to the case where the transform was based on the masking threshold computed over the sinusoidal coded part only. Finally, we compared our method with the filterbank approach [3], after having removed the perceptual irrelevant sinusoids with the algorithm described in [5].…”

Section: Resultsmentioning

confidence: 99%

“…Rather than minimizing the well known l2 distortion measure as in (1), we minimize in this paper a perceptual relevant distortion measure. This new approach has the advantage that no preprocessing step is needed to remove the remaining perceptually irrelevant tonal components as in [5], since the perceptual distortion measure will automatically recognize that those components have no perceptual relevance and will, therefore, not be modelled.…”

Section: Residual Coding With Perceptual Lpcmentioning

confidence: 99%

“…One way to overcome the above mentioned problem is to use a method as described in [5] where perceptual irrelevant sinusoids are removed from the target signal using an iterative procedure, prior to noise coding. The iterative procedure is terminated based on a heuristically determined criterion.…”

Section: Introductionmentioning

confidence: 99%

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Perceptual linear predictive noise modelling for sinusoid-plus-noise audio coding

Hendriks

Heusdens

Jensen

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing

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Sinusoidal coding of audio subject to a bit-rate constraint will in general result in a noise-like residual signal. This residual signal is of high perceptual importance; reconstruction of audio using the sinusoidal representation only will typically result in an artificial sounding reconstruction. In this paper we present a method, called perceptual linear predictive coding (PLPC), where the residual is encoded by applying LPC in the perceptual domain. This method minimizes a perceptual modelling error and therefore represents only residual components that are of perceptual relevance, while automatically discarding components masked by the sinusoidal coded part. Subjective listening tests show that PLPC performs significantly better than ordinary LPC as a sinusoidal residual coding technique. Furthermore, PLPC combined with a flexible segmentation and model order allocation algorithm leads to a significant gain in terms of R/D performance for fragments with fast changing characteristics.

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Section: Resultsmentioning

confidence: 99%

Section: Residual Coding With Perceptual Lpcmentioning

confidence: 99%