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Reverse engineering of an audio mix using neural networks
Čermák, Jerguš ; Mokrý, Ondřej (referee) ; Rajmic, Pavel (advisor)
This bachelor's thesis focuses on the use of neural network algorithms to determine the parameters of signal processors used in the mixing of audio tracks. The thesis presents linear signal processors such as \emph{Gain}, \emph{Pan}, \emph{Filter}, and \emph{Reverb}, which are commonly used to process audio signal and to produce a stereo mix of the audio recording. These processors are subsequently used within the neural network model as layers, implemented using the DDSP library, aimed at predicting the parameters used in the mix, given the knowledge of the input tracks and the target mix. Resultantly, stereo mixdowns were created, and their parameters were estimated using two neural network models. The results were evaluated using both objective measurements and subjective methods (listening test).
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