  Singing melody extraction is an important problem in the field of music
information retrieval. Existing methods typically rely on frequency-domain
representations to estimate the sung frequencies. However, this design does not
lead to human-level performance in the perception of melody information for
both tone (pitch-class) and octave. In this paper, we propose TONet, a
plug-and-play model that improves both tone and octave perceptions by
leveraging a novel input representation and a novel network architecture.
First, we present an improved input representation, the Tone-CFP, that
explicitly groups harmonics via a rearrangement of frequency-bins. Second, we
introduce an encoder-decoder architecture that is designed to obtain a salience
feature map, a tone feature map, and an octave feature map. Third, we propose a
tone-octave fusion mechanism to improve the final salience feature map.
Experiments are done to verify the capability of TONet with various baseline
backbone models. Our results show that tone-octave fusion with Tone-CFP can
significantly improve the singing voice extraction performance across various
datasets -- with substantial gains in octave and tone accuracy.
