  In this paper we present an attentional neural network for folk song
classification. We introduce the concept of musical motif embedding, and show
how using melodic local context we are able to model monophonic folk song
motifs using the skipgram version of the word2vec algorithm. We use the motif
embeddings to represent folk songs from Germany, China, and Sweden, and
classify them using an attentional neural network that is able to discern
relevant motifs in a song. The results show how the network obtains state of
the art accuracy in a completely unsupervised manner, and how motif embeddings
produce high quality motif representations from folk songs. We conjecture on
the advantages of this type of representation in large symbolic music corpora,
and how it can be helpful in the musicological analysis of folk song
collections from different cultures and geographical areas.
