4-17 - A Deep Learning Based Analysis-synthesis Framework for Unison Singing
Pritish Chandna, Helena Cuesta, Emilia Gomez
Keywords: MIR tasks, Music synthesis and transformation, Applications, Music retrieval systems, Evaluation, datasets, and reproducibility, Human-centered MIR, User-centered evaluation, Musical features and properties, Expression and performative aspects of music
Abstract:
Unison singing is the name given to an ensemble of singers simultaneously singing the same melody and lyrics. While each individual singer in a unison sings the same principle melody, there are slight timing and pitch deviations between the singers, which, along with the ensemble of timbres, give the listener a perceived sense of "unison". In this paper, we present a study of unison singing in the context of choirs; utilising some recently proposed deep-learning based methodologies, we analyse the fundamental frequency (F0) distribution of the individual singers in recordings of unison mixtures. Based on the analysis, we propose a system for synthesising a unison signal from an a cappella input and a single voice prototype representative of a unison mixture. We use subjective listening test to evaluate perceptual factors of our proposed system for synthesis, including quality, adherence to the melody as well the degree of perceived unison.