# A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics

arXiv: https://arxiv.org/abs/2103.12615 · HTML: https://mlsanity.com/paper/2103.12615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Generating Images with Perceptual Similarity Metrics based on Deep Networks](https://mlsanity.com/paper/1602.02644/) (2016)
- [Variational Recurrent Auto-Encoders](https://mlsanity.com/paper/1412.6581/) (2014)
- [GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures](https://mlsanity.com/paper/1707.04588/) (2017)
- [DopeLearning: A Computational Approach to Rap Lyrics Generation](https://mlsanity.com/paper/1505.04771/) (2015)
- [Chinese Song Iambics Generation with Neural Attention-based Model](https://mlsanity.com/paper/1604.06274/) (2016)
- [Music generation with variational recurrent autoencoder supported by history](https://mlsanity.com/paper/1705.05458/) (2017)
- [Lyrics-Based Music Genre Classification Using a Hierarchical Attention Network](https://mlsanity.com/paper/1707.04678/) (2017)
- [KATE: K-Competitive Autoencoder for Text](https://mlsanity.com/paper/1705.02033/) (2017)
- [Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting](https://mlsanity.com/paper/1612.03205/) (2016)
- [Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks](https://mlsanity.com/paper/1711.07798/) (2017)
- [The Evolution of Popular Music: USA 1960-2010](https://mlsanity.com/paper/1502.05417/) (2015)

## Abstract (arXiv)

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.