AI summary

This paper introduces an audio chord recognition system using a recurrent neural network that is optimized with a combination of chromagram targets and chord information. An efficient algorithm is devised to search for the global mode of the output distribution. The method achieves competitive results on the MIREX dataset.

Abstract

In this paper, we present an audio chord recognition system based on a recurrent neural network. The audio features are obtained from a deep neural network optimized with a combination of chromagram targets and chord information, and aggregated over different time scales. Contrarily to other existing approaches, our system incorporates acoustic and musicological models under a single training objective. We devise an efficient algorithm to search for the global mode of the output distribution while taking long-term dependencies into account. The resulting method is competitive with state-of-the-art approaches on the MIREX dataset in the major/minor prediction task.