This paper compares three state-of-the-art stimuli (multitone-pink, MLS, and log-sweep) to simultaneously deconvolve the impulse responses from several loudspeakers. A hyperparameter optimization algorithm constructs the stimulus, where the algorithm optimizes the stimulus parameters by minimizing a time domain error between the actual impulse responses and the simultaneously deconvolved responses over a training dataset. Objective results are presented for the various stimuli in a test data set that demonstrate the efficacy of each stimulus in the context of simultaneous deconvolution.
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