  This paper presents a convolutional neural network (CNN) that uses input from
a polyphonic pitch estimation system to predict perceived minor/major modality
in music audio. The pitch activation input is structured to allow the first CNN
layer to compute two pitch chromas focused on different octaves. The following
layers perform harmony analysis across chroma and time scales. Through max
pooling across pitch, the CNN becomes invariant with regards to the key class
(i.e., key disregarding mode) of the music. A multilayer perceptron combines
the modality activation output with spectral features for the final prediction.
The study uses a dataset of 203 excerpts rated by around 20 listeners each, a
small challenging data size requiring a carefully designed parameter sharing.
With an R2 of about 0.71, the system clearly outperforms previous systems as
well as individual human listeners. A final ablation study highlights the
importance of using pitch activations processed across longer time scales, and
using pooling to facilitate invariance with regards to the key class.
