# Improving Polyphonic Music Models with Feature-Rich Encoding Source: [Improving Polyphonic Music Models with Feature-Rich Encoding]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:11:22.164Z Authors: Omar Peracha Published: 2019-11-26T18:38:30.000Z ## Abstract This paper explores sequential modelling of polyphonic music with deep neural networks. While recent breakthroughs have focussed on network architecture, we demonstrate that the representation of the sequence can make an equally significant contribution to the performance of the model as measured by validation set loss. By extracting salient features inherent to the training dataset, the model can either be conditioned on these features or trained to predict said features as extra components of the sequences being modelled. We show that training a neural network to predict a seemingly more complex sequence, with extra features included in the series being modelled, can improve overall model performance significantly. We first introduce TonicNet, a GRU-based model trained to initially predict the chord at a given time-step before then predicting the notes of each voice at that time-step, in contrast with the typical approach of predicting only the notes. We then evaluate TonicNet on the canonical JSB Chorales dataset and obtain state-of-the-art results. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Music Modeling on J.S. Bach Chorales 16th notes (val)](): Music Modeling; J.S. Bach Chorales 16th notes (val); Validation NLL: 0.22. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()