|
|
Machine learning in audio effects
Sychra, Jakub ; Mošner, Ladislav (oponent) ; Černocký, Jan (vedoucí práce)
Reverse engineering audio effects from mixed tracks is a complex topic requiring signal processing and music engineering experience. This work aims at creation of a system capable of identifying the sequence and parameters of guitar effects from a mixed audio track. Training data was created using clean guitar sounds from IDMT-SMT-Audio-Effects, augmented by known effects (BitCrush, Chorus, Clipping, Compressor, Delay, Distortion, High-pass filter, Ladder filter, Low-pass filter, Limiter, Phaser and Reverb), all implemented with a Python wrapper around standard VST effects. The system is based on VGGish neural network architecture with several classification (presence of effects) and regression (parameters of effects) heads. The performance of the algorithm is evaluated on classification and regression accuracy, as well as in informal listening tests.
|
host ::
RSS.