# Song From PI: A Musically Plausible Network for Pop Music Generation

arXiv: https://arxiv.org/abs/1611.03477 · HTML: https://mlsanity.com/paper/1611.03477/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2016.

## Built on by (most cited first, 13 of 13)

- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [Music Composition with Deep Learning: A Review](https://mlsanity.com/paper/2108.12290/) (2021)
- [A Review of Intelligent Music Generation Systems](https://mlsanity.com/paper/2211.09124/) (2022)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [A Survey of Deep Learning Audio Generation Methods](https://mlsanity.com/paper/2406.00146/) (2024)
- [Unsupervised Symbolic Music Segmentation using Ensemble Temporal Prediction Errors](https://mlsanity.com/paper/2207.00760/) (2022)
- [A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives](https://mlsanity.com/paper/2210.13944/) (2022)
- [Graph-based Polyphonic Multitrack Music Generation](https://mlsanity.com/paper/2307.14928/) (2023)
- [A Survey of Music Generation in the Context of Interaction](https://mlsanity.com/paper/2402.15294/) (2024)
- [What Users Want? WARHOL: A Generative Model for Recommendation](https://mlsanity.com/paper/2109.01093/) (2021)
- [Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models](https://mlsanity.com/paper/2211.00731/) (2022)
- [Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics](https://mlsanity.com/paper/2301.10015/) (2023)
- [RPPNet: Perceptually-Grouped Rhythm-Pitch Primitives for Long-Term Structure Melody Generation via Boundary-Aware Modeling](https://mlsanity.com/paper/2607.19776/) (2026)

## Abstract (arXiv)

We present a novel framework for generating pop music. Our model is a hierarchical Recurrent Neural Network, where the layers and the structure of the hierarchy encode our prior knowledge about how pop music is composed. In particular, the bottom layers generate the melody, while the higher levels produce the drums and chords. We conduct several human studies that show strong preference of our generated music over that produced by the recent method by Google. We additionally show two applications of our framework: neural dancing and karaoke, as well as neural story singing.