The aim of this paper was to create an automatic sound source classification framework for recordings captured with a microphone array and evaluate the sound source separation algorithm impact on the classification results. The preprocessing related to the said evaluation concerned convolving the dataset samples with impulse responses captured with a microphone array, as well as mixing the samples together to simulate their co-presence in a virtual recording scene. This way, the evaluation of the separation algorithm impact on classification results was possible. Furthermore, such approach saved multiple hours of labour that would need to be spent on the recording process itself. Finally, the classification results delivered by different models were evaluated and compared.
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