In this article, we propose a control strategy for synthesized continuous-interaction sounds. The framework of our research is based on the action–object paradigm that describes the sound as the result of an action on an object and that presumes the existence of sound invariants (i.e., perceptually relevant signal morphologies that carry information about the action's or the object's attributes). Auditory cues are investigated here for the evocations of rubbing, scratching, and rolling interactions. A generic sound-synthesis model that simulates these interactions is detailed. We then suggest an intuitive control strategy that enables users to navigate continuously from one interaction to another in an “action space,” thereby offering the possibility to simulate morphed interactions—for instance, ones that morph between rubbing and rolling.
Featured Application: The proposed framework is highly suitable for audio applications that require analysis-synthesis systems with the following properties: stability, perfect reconstruction, and a flexible choice of redundancy.Abstract: Many audio applications rely on filter banks (FBs) to analyze, process, and re-synthesize sounds. For these applications, an important property of the analysis-synthesis system is the reconstruction error; it has to be minimized to avoid audible artifacts. Other advantageous properties include stability and low redundancy. To exploit some aspects of auditory perception in the signal chain, some applications rely on FBs that approximate the frequency analysis performed in the auditory periphery, the gammatone FB being a popular example. However, current gammatone FBs only allow partial reconstruction and stability at high redundancies. In this article, we construct an analysis-synthesis system for audio applications. The proposed system, referred to as Audlet, is an oversampled FB with filters distributed on auditory frequency scales. It allows perfect reconstruction for a wide range of FB settings (e.g., the shape and density of filters), efficient FB design, and adaptable redundancy. In particular, we show how to construct a gammatone FB with perfect reconstruction. Experiments demonstrate performance improvements of the proposed gammatone FB when compared to current gammatone FBs in terms of reconstruction error and stability, especially at low redundancies. An application of the framework to audio source separation illustrates its utility for audio processing.
Abstract-Sinusoidal modeling is one of the most popular techniques for low bitrate audio coding. Usually, the sinusoidal parameters (amplitude, pulsation and phase of each sinusoidal component) are kept constant within a time segment. An alternative model, the so-called Exponentially-Damped Sinusoidal (EDS) model, includes an additional damping parameter for each sinusoidal component to better represent the signal characteristics. It was however never shown that the EDS model could be efficient for perceptual audio coding. To that aim, we propose in this paper an efficient analysis/synthesis framework with dynamic timesegmentation on transients and psychoacoustic modeling, and an asymptotically optimal entropy-constrained quantization method for the four sinusoid parameters (e.g including damping). We then apply this coding technique to real audio excerpts for a given entropy target corresponding to a low bitrate (20 kbits/s), and compare this method with a classical sinusoidal coding scheme using a constant-amplitude sinusoidal model and the perceptually weighted Matching Pursuit algorithm. Subjective listening tests show that the EDS model is more efficient on audio samples with fast transient content, and similar to the classical model for more stationary audio samples.
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