Singing together: Pitch accuracy and interaction in unaccompanied unison and duet singing

Abstract

We investigate singing interaction by analysis of the factors influencing pitch accuracy of unaccompanied duet singers. Eight pairs of singers sang two excerpts either in unison or two-part harmony. The experimental condition varied which singers could hear their partners. After semi-automatic pitch-tracking and manual checking, we calculated the pitch error and interval error, and tested the factors of influence using a one-way ANOVA. The results indicate that: 1) singing with the same vocal part is more accurate than singing with a different vocal part; 2) singing solo has less pitch error than singing with a partner; 3) pitch errors are correlated, as singers adjust their pitch to mitigate their partner's error and preserve harmonic intervals at the expense of melodic intervals and absolute pitch; 4) other factors influence the pitch accuracy, including: score pitch, score harmonic interval, score melodic interval, musical background, vocal part and individual differences.

Intelligent Control System Algorithm Based on Cooperation in Singing Method of Soloist

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“…Molchanova Violetta S discussed the singing lesson system of educational institutions in the Russian Empire, focusing on the history and educational significance of school music and chorus training, teaching difficulties and methods [1]. DaiJiajie studied the interactivity of singing by analyzing the factors affecting the intonation of an unaccompanied duet, calculated the pitch error and interval error, and tested the influencing factors using one-way analysis of variance and linear mixed effects model [2]. Yehia Enas Fares offers a comprehensive overview of the etiquette, customs and rules of courtesy in ancient and modern Egyptian female solo vocations from a comparative perspective.…”

Voice Assignment in Vocal Quartets Using Deep Learning Models Based on Pitch Salience

“…Despite this popularity, Music Information Retrieval (MIR) research on polyphonic music has been rather constrained to other types of musical material such as pop/rock (Ryynänen and Klapuri, 2008;Bittner et al, 2017), jazz (Abeßer et al, 2017;Abeßer and Müller, 2021), or piano music (Sigtia et al, 2016;Nakamura et al, 2018) in the past decades. However, several works have recently addressed singers' intonation and interaction in vocal ensembles (Devaney et al, 2012;Dai and Dixon, 2019;Cuesta et al, 2018;Weißet al, 2019), the analysis of vocal unisons (Cuesta et al, 2018(Cuesta et al, , 2019Chandna et al, 2020), the estimation of multiple fundamental frequency (F0) values from polyphonic vocal performances (Su et al, 2016;Schramm and Benetos, 2017;McLeod et al, 2017;Cuesta et al, 2020), or the separation of singing voices (Gover and Depalle, 2020;Petermann et al, 2020;Sarkar et al, 2020). Most of the tasks mentioned above commonly rely either on analyzing separate audio tracks (stems) for each singer of the ensemble, or on the ground truth individual F0 contours.…”

A Deep-Learning Based Framework for Source Separation, Analysis, and Synthesis of Choral Ensembles

“…Over the last few years, the MIR community has shown an increasing interest on the topic, particularly with the emergence of data-driven machine learning methodologies, as well as novel datasets. Studies on singers' intonation and interaction in vocal ensembles (Devaney et al, 2012;Dai and Dixon, 2017;Cuesta et al, 2018;Dai and Dixon, 2019;Weiss et al, 2019), the analysis and synthesis of vocal unisons (Cuesta et al, 2018;Cuesta et al, 2019;Chandna et al, 2020), multi-pitch estimation of polyphonic vocal performances (Su et al, 2016;Schramm and Benetos, 2017;Cuesta et al, 2020), automatic transcription (McLeod et al, 2017), or the separation of voices (Gover and Depalle, 2020;Petermann et al, 2020;Sarkar et al, 2020) have been published recently.…”