2016/09/05 by Léopold Crestel, Crestel, Léopold, Philippe Esling +1 · 1 citation
Computer Science · #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #cs.LG
paper · pdf · doi:10.48550/arxiv.1609.01203
openalex publication_date 2016/09/05 · arxiv created 2017/05/18 · arxiv updated 2017/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper introduces the first system for performing automatic orchestration based on a real-time piano input. We believe that it is possible to learn the underlying regularities existing between piano scores and their orchestrations by notorious composers, in order to automatically perform this task on novel piano inputs. To that end, we investigate a class of statistical inference models called conditional Restricted Boltzmann Machine (cRBM). We introduce a specific evaluation framework for orchestral generation based on a prediction task in order to assess the quality of different models. As prediction and creation are two widely different endeavours, we discuss the potential biases in evaluating temporal generative models through prediction tasks and their impact on a creative system. Finally, we introduce an implementation of the proposed model called Live Orchestral Piano (LOP), which allows to perform real-time projective orchestration of a MIDI keyboard input.