Automatic Classification of Chains of Guitar Effects Through Evolutionary Neural Architecture Search
Recent studies on classifying electric guitar effects have achieved
high accuracy, particularly with deep learning techniques. However, these studies often rely on simplified datasets consisting
mainly of single notes rather than realistic guitar recordings.
Moreover, in the specific field of effect chain estimation, the literature tends to rely on large models, making them impractical for
real-time or resource-constrained applications. In this work, we
recorded realistic guitar performances using four different guitars
and created three datasets by applying a chain of five effects with
increasing complexity: (1) fixed order and parameters, (2) fixed order with randomly sampled parameters, and (3) random order and
parameters. We also propose a novel Neural Architecture Search
method aimed at discovering accurate yet compact convolutional
neural network models to reduce power and memory consumption.
We compared its performance to a basic random search strategy,
showing that our custom Neural Architecture Search outperformed
random search in identifying models that balance accuracy and
complexity. We found that the number of convolutional and pooling layers becomes increasingly important as dataset complexity
grows, while dense layers have less impact. Additionally, among
the effects, tremolo was identified as the most challenging to classify.