The decomposition of audio signals into perceptually meaningful modulation components is highly desirable for the development of new audio effects on the one hand and as a building block for future efficient audio compression algorithms on the other hand. In the past, there has always been a distinction between parametric coding methods and waveform coding: While waveform coding methods scale easily up to transparency (provided the necessary bit rate is available), parametric coding schemes are subjected to the limitations of the underlying source models. Otherwise, parametric methods usually offer a wealth of manipulation possibilities which can be exploited for application of audio effects, while waveform coding is strictly limited to the best as possible reproduction of the original signal. The analysis/synthesis approach presented in this paper is an attempt to show a way to bridge this gap by enabling a seamless transition between both approaches.

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Modern music production and sound generation often relies on manipulation of pre-recorded pieces of audio, so-called samples, taken from a huge database. Consequently, there is a increasing request to extensively adapt these samples to any new musical context in a flexible way. For this purpose, advanced digital signal processing is needed in order to realize audio effects like pitch shifting, time stretching or harmonization. Often, a key part of these processing methods is a signal adaptive, block based spectral segmentation operation. Hence, we propose a novel algorithm for such a spectral segmentation based on local centers of gravity (COG). The method was originally developed as part of a multiband modulation decomposition for audio signals. Nevertheless, this algorithm can also be used in the more general context of improved vocoder related applications.

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In previous papers, the concept of the modulation vocoder (MODVOC) has been introduced and its general capability to perform a selective transposition on polyphonic music content has been pointed out. This renders applications possible which aim at changing the key mode of pre-recorded PCM music samples. In this paper, two enhancement techniques for selective pitch transposition by the MODVOC are proposed. The performance of the selective transposition application and the merit of these techniques are benchmarked by results obtained from a specially designed listening test methodology which is capable to govern extreme changes in terms of pitch with respect to the original audio stimuli. Results of this subjective perceptual quality assessment are presented for items that have been converted between minor and major key mode by the MODVOC and, additionally, by the first commercially available software which is also capable of handling this task.

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In this work we present a new scenario of analyzing and separating linear mixtures of musical instrument signals. When instruments are playing in unison, traditional source separation methods are not performing well. Although the sources share the same pitch, they often still differ in their modulation frequency caused by vibrato and/or tremolo effects. In this paper we propose source separation schemes that exploit AM/FM characteristics to improve the separation quality of such mixtures. We show a method to process mixtures based on differences in their amplitude modulation frequency of the sources by using non-negative tensor factorization. Further, we propose an informed warped time domain approach for separating mixtures based on variations in the instantaneous frequencies of the sources.

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