An Attentional Neural Network Architecture for Folk Song Classification
This provides a tool for musicological analysis of folk songs across cultures, though it is incremental in applying existing NLP techniques to music.
The paper tackles folk song classification by introducing musical motif embeddings using word2vec and an attentional neural network, achieving state-of-the-art accuracy in an unsupervised manner.
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.