  Are similar, or even identical, mechanisms used in the computational modeling
of speech segmentation, serial image processing and music processing? We
address this question by exploring how TRACX2, (French et al., 2011; French \&
Cottrell, 2014; Mareschal \& French, 2017), a recognition-based, recursive
connectionist autoencoder model of chunking and sequence segmentation, which
has successfully simulated speech and serial-image processing, might be applied
to elementary melody perception. The model, a three-layer autoencoder that
recognizes ''chunks'' of short sequences of intervals that have been frequently
encountered on input, is trained on the tone intervals of melodically simple
French children's songs. It dynamically incorporates the internal
representations of these chunks into new input. Its internal representations
cluster in a manner that is consistent with ''human-recognizable'' melodic
categories. TRACX2 is sensitive to both contour and proximity information in
the musical chunks that it encounters in its input. It shows the
''end-of-word'' superiority effect demonstrated by Saffran et al. (1999) for
short musical phrases. The overall findings suggest that the recursive
autoassociative chunking mechanism, as implemented in TRACX2, may be a general
segmentation and chunking mechanism, underlying not only word-and
imagechunking, but also elementary melody processing.
