  Learning and analyzing rap lyrics is a significant basis for many web
applications, such as music recommendation, automatic music categorization, and
music information retrieval, due to the abundant source of digital music in the
World Wide Web. Although numerous studies have explored the topic, knowledge in
this field is far from satisfactory, because critical issues, such as prosodic
information and its effective representation, as well as appropriate
integration of various features, are usually ignored. In this paper, we propose
a hierarchical attention variational autoencoder framework (HAVAE), which
simultaneously consider semantic and prosodic features for rap lyrics
representation learning. Specifically, the representation of the prosodic
features is encoded by phonetic transcriptions with a novel and effective
strategy~(i.e., rhyme2vec). Moreover, a feature aggregation strategy is
proposed to appropriately integrate various features and generate
prosodic-enhanced representation. A comprehensive empirical evaluation
demonstrates that the proposed framework outperforms the state-of-the-art
approaches under various metrics in different rap lyrics learning tasks.
