DopeLearning: A Computational Approach to Rap Lyrics Generation (1505.04771v2)

Abstract: Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.

• The paper introduces an innovative computational approach combining RankSVM and deep neural networks to synthesize rap lyrics by selecting contextually relevant lines.
• It achieves a 17% prediction accuracy and produces lyrics with a 21% increase in rhyme density compared to top human rappers, demonstrating its technical effectiveness.
• The approach is operationalized in a web tool, highlighting its potential for broader applications in automated content creation and computational creativity.

A Computational Approach to Rap Lyrics Generation

This essay examines a unique approach to generating rap lyrics, detailed in the paper titled "DopeLearning: A Computational Approach to Rap Lyrics Generation." This research undertakes the challenge of synthesizing rap lyrics by leveraging advanced machine learning techniques, specifically combining the RankSVM algorithm and a deep neural network to capture the dual requirements of narrative creativity and complex rhyme patterns inherent in rap music.

Summary of Methods and Results

The paper's central innovation is an information retrieval-based method that constructs new lyrics by selecting the most relevant next line based on a given sequence of previous lines. The dataset used comprises over half a million lines from 104 rap artists, which provides a robust foundation for training the model. The relevance of the candidate lines is evaluated by a predictive model that fuses features drawn from traditional information retrieval, deep learning, and linguistic structures particular to the rap genre.

1. Features and Machine Learning Model:
   • The features are categorized into rhyming, structural, and semantic similarities. The rhyming features include measures like EndRhyme and OtherRhyme, while structural similarity is captured through features like LineLength. Semantic similarity leverages both bag-of-word models and latent semantic analysis.
   • A novel deep neural network model is introduced for semantic feature extraction, employing a fixed-length feed-forward neural network structure that maps sequences of lines into a high-dimensional vector space.
2. Performance and Evaluation:
   • The model achieves a 17% accuracy in predicting the true next line among 299 randomly selected alternatives, significantly outperforming the baseline random prediction accuracy of 0.3%.
   • The generated lyrics surpass the top human rappers by 21% in rhyme density. This metric, validated through human subject experiments, effectively estimated the technical quality of the lyrics from a rhyming perspective.
3. Deployment and User Interaction:
   • The methodology has been operationalized in a web tool named DeepBeat, which demonstrates strong correlation between machine-learned rankings and user preferences as per usage logs. This correlation emphasizes the efficacy of the model in aligning with human perceptions of lyrical quality.

Implications and Future Directions

The implications of this research extend beyond rap lyrics generation. The effective use of neural networks and information retrieval techniques in this context suggests potential applications in broader text synthesis tasks, such as automated content creation and conversational response generation. The methods and models outlined could be adapted to similar creative fields, enhancing the utility of computational creativity tools.

Future research could aim at developing models capable of generating entirely novel lines, possibly integrating advancements in generative models such as GPT variants for word-level lyric generation. Another promising direction is the automatic generation of thematic and cohesive storylines within songs, enhancing the narrative coherence of machine-generated content.

In summary, this paper's approach combines computational creativity with practical application, yielding an algorithm that not only performs well against human benchmarks but also provides a versatile framework for further explorations in artificial intelligence-driven content creation.