Paper Archive

There are several algorithms and approaches to Room Impulse Response (RIR) estimation. To the best of the author’s knowledge, there is no documentation of accuracy, speed, or even the feasibility of using signed distance functions (SDFs) in combination with sphere tracing for this task. A proof of concept with a focus on real-time performance is presented here, which still lacks many features such as frequency-dependent absorption and scattering coefficients, arbitrary source and receiver directives etc. The results are then compared to real room impulse responses and to a different simulation algorithm. Also, the rather special merits of such an approach, such as 4D reverberation and simple rounding of geometry, are briefly discussed and presented.

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Reverb provides psychoacoustic cues that convey information concerning relative locations within an acoustical space. The need arises often in audio production to impart an acoustic context on an audio track that resembles a reference track. One tool for making audio tracks appear to be recorded in the same space is by applying reverb to a dry track that is similar to the reverb in a wet one. This paper presents a model for the task of “reverb matching,” where we attempt to automatically add artificial reverb to a track, making it sound like it was recorded in the same space as a reference track. We propose a model architecture for performing reverb matching and provide subjective experimental results suggesting that the reverb matching model can perform as well as a human. We also provide open source software for generating training data using an arbitrary Virtual Studio Technology plug-in.

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Modern commodity GPUs offer high numerical throughput per unit of cost, but often sit idle during audio workstation tasks. Various researches in the field have shown that GPUs excel at tasks such as Finite-Difference Time-Domain simulation and wavefield synthesis. Concrete implementations of several such projects are available for use. Benchmarks and use cases generally concentrate on running one project on a GPU. Running multiple such projects simultaneously is less common, and reduces throughput. In this work we list some concerns when running multiple heterogeneous tasks on the GPU. We apply optimization strategies detailed in developer documentation and commercial CUDA literature, and show results through the lens of real-time audio tasks. We benchmark the cases of (i) a homogeneous effect chain made of previously separate effects, and (ii) a synthesizer with distinct, parallelizable sound generators.

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