# Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN

arXiv: https://arxiv.org/abs/1412.7927 · HTML: https://mlsanity.com/paper/1412.7927/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2014.

## Built on by (most cited first, 6 of 6)

- [Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model](https://mlsanity.com/paper/2311.00968/) (2023)
- [Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey](https://mlsanity.com/paper/2402.17467/) (2024)
- [Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild](https://mlsanity.com/paper/2107.09510/) (2021)
- [A Survey of Music Generation in the Context of Interaction](https://mlsanity.com/paper/2402.15294/) (2024)
- [CoCoFormer: A controllable feature-rich polyphonic music generation method](https://mlsanity.com/paper/2310.09843/) (2023)
- [TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization](https://mlsanity.com/paper/2509.00914/) (2025)

## Abstract (arXiv)

In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state of the RNN that allows it to provide temporal information and a multi-layer DBN that helps in high level representation of the data. This makes RNN-DBNs ideal for sequence generation. Further, the use of a DBN in conjunction with the RNN makes this model capable of significantly more complex data representation than an RBM. We apply this technique to the task of polyphonic music generation.