This paper describes the development and application of a machine listening system for the automated testing of implementation equivalence in music signal processing effects which contain a high level of randomized time variation. We describe a mathematical model of generalized randomization in audio effects and explore different representations of the effect’s data. We then propose a set of classifiers to reliably determine if two implementations of the same randomized audio effect are functionally equivalent. After testing these classifiers against each other and against a set of human listeners we find the best implementation and determine that it agrees with the judgment of human listeners with an F1-Score of 0.8696.

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This paper describes a modification of the ESPRIT algorithm which can be used to determine the parameters (frequency, decay time, initial magnitude and initial phase) of a modal reverberator that best match a provided room impulse response. By applying perceptual criteria we are able to match room impulse responses using a variable number of modes, with an emphasis on high quality for lower mode counts; this allows the synthesis algorithm to scale to different computational environments. A hybrid FIR/modal reverb architecture is also presented which allows for the efficient modeling of room impulse responses that contain sparse early reflections and dense late reverb. MUSHRA tests comparing the analysis/synthesis using various mode numbers for our algorithms, and for another state of the art algorithm, are included as well.

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