Acceleration of audio workloads on generally-programmable GPU (GPGPU) hardware offers potentially high speedup factors, but also presents challenges in terms of development and deployment. We can increasingly depend on such hardware being available in users’ systems, yet few real-time audio products use this resource. We propose a suite of benchmarks to qualify a GPU as suitable for batch or real-time audio processing. This includes both microbenchmarks and higher-level audio domain benchmarks. We choose metrics based on application, paying particularly close attention to latency tail distribution. We propose an extension to the benchmark framework to more accurately simulate the real-world request pattern and performance requirements when running in a digital audio workstation. We run these benchmarks on two common consumer-level platforms: a PC desktop with a recent midrange discrete GPU and a Macintosh desktop with unified CPUGPU memory architecture.

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This paper proposes a real-time implementation of a linear-phase octave graphic equalizer (GEQ), previously introduced by the same authors. The structure of the GEQ is based on interpolated finite impulse response (IFIR) filters and is derived from a single prototype FIR filter. The low computational cost and small latency make the presented GEQ suitable for real-time applications. In this work, the GEQ has been implemented as a plugin of a specific software, used for real-time tests. The performance of the equalizer has been evaluated through subjective tests, comparing it with a filterbank equalizer. For the tests, four standard equalization curves have been chosen. The experimental results show promising outcomes. The result is an accurate real-time-capable linear-phase GEQ with a reasonable latency.

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Stereo widening algorithms aim to extend the stereo image width and thereby, increase the perceived spaciousness of a mix. Here, we present the design and implementation of a stereo widening plugin that is computationally efficient. First, a stereo signal is decorrelated by convolving with a velvet noise sequence, or alternately, by passing through a cascade of allpass filters with randomised phase. Both the original and decorrelated signals are passed through perfect reconstruction filterbanks to get a set of lowpassed and highpassed signals. Then, the original and decorrelated filtered signals are combined through a mixer and summed to produce the final stereo output. Two separate parameters control the perceived width of the lower frequencies and higher frequencies respectively. A transient detection block prevents the smearing of percussive signals caused by the decorrelation filters. The stereo widener has been released as an open-source plugin.

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We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the efficient parallel computation of input graphs, signals, and processor parameters in GPU. Then, we show its example use under a music mixing scenario, where parameters of every differentiable processor in a large graph are optimized via gradient descent. The code is available at https://github.com/sh-lee97/grafx.

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LTFATPY is a software package for accessing the Large Time Frequency Analysis Toolbox (LTFAT) from Python. Dedicated to time-frequency analysis, LTFAT comprises a large number of linear transforms for Fourier, Gabor, and wavelet analysis along with their associated operators. Its filter bank module is a collection of computational routines for finite impulse response and band-limited filters, allowing for the specification of constant-Q and auditory-inspired transforms. While LTFAT has originally been written in MATLAB/GNU Octave, the recent popularity of the Python programming language in related fields, such as signal processing and machine learning, makes it desirable to have LTFAT available in Python as well. We introduce LTFATPY, describe its main features, and outline further developments.

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