When convolutional neural networks are used to tackle learning problems based
on music or, more generally, time series data, raw one-dimensional data are
commonly pre-processed to obtain spectrogram or mel-spectrogram coefficients,
which are then used as input to the actual neural network. In this
contribution, we investigate, both theoretically and experimentally, the
influence of this pre-processing step on the network's performance and pose the
question, whether replacing it by applying adaptive or learned filters directly
to the raw data, can improve learning success. The theoretical results show
that approximately reproducing mel-spectrogram coefficients by applying
adaptive filters and subsequent time-averaging is in principle possible. We
also conducted extensive experimental work on the task of singing voice
detection in music. The results of these experiments show that for
classification based on Convolutional Neural Networks the features obtained
from adaptive filter banks followed by time-averaging perform better than the
canonical Fourier-transform-based mel-spectrogram coefficients. Alternative
adaptive approaches with center frequencies or time-averaging lengths learned
from training data perform equally well.Comment: Completely revised version; 21 pages, 4 figure
