Download On Stretching Gaussian Noises with the Phase Vocoder Recently, the processing of non-sinusoidal signals, or sound textures, has become an important topic in various areas. In general, the transformation is done by the phase vocoder techniques. Since the phase vocoder technique is based on a sinusoidal model, it’s performance is not satisfying when applied to transform sound textures. The following article investigates into the problem using as example the most basic non-sinusoidal sounds, that are noise signals. We demonstrate the problems that arise when time stretching noise with the phase vocoder, provide a description of some relevant statistical properties of the time frequency representation of noise and introduce an algorithm that allows to preserve these statistical properties when time stretching noise with the phase vocoder. The resulting algorithm significantly improves the perceptual quality of the time stretched noise signals and therefore it is seen as a promising first step towards an algorithm for transformation of sound textures.
Download On the Modeling of Sound Textures Based on the STFT Representation Sound textures are often noisy and chaotic. The processing of these sounds must be based on the statistics of its corresponding time-frequency representation. In order to transform sound textures with existing mechanisms, a statistical model based on the STFT representation is favored. In this article, the relation between statistics of a sound texture and its time-frequency representation is explored. We proposed an algorithm to extract and modify the statistical properties of a sound texture based on its STFT representation. It allows us to extract the statistical model of a sound texture and resynthesise the sound texture after modifications have been made. It could also be used to generate new samples of the sound texture from a given sample. The results of the experiment show that the algorithm is capable of generating high quality sounds from an extracted model. This result could serve as a basis for transformations like morphing or high-level control of sound textures.
Download Timbre-Constrained Recursive Time-Varying Analysis for Musical Note Separation Note separation in music signal processing becomes difficult when there are overlapping partials from co-existing notes produced by either the same or different musical instruments. In order to deal with this problem, it is necessary to involve certain invariant features of musical instrument sounds into the separation processing. For example, the timbre of a note of a musical instrument may be used as one possible invariant feature. In this paper, a timbre estimate is used to represent this feature such that it becomes a constraint when note separation is performed on a mixture signal. To demonstrate the proposed method, a timedependent recursive regularization analysis is employed. Spectral envelopes of different notes are estimated and a modified parameter update strategy is applied to the recursive regularization process. The experiment results show that the flaws due to the overlapping partial problem can be effectively reduced through the proposed approach.