Dynamics are one of the fundamental tools of expressivity in a performance. While the usage of this tool is highly subjective, a systematic methodology to derive loudness markings based on a performance can be highly beneficial. With this goal in mind, this paper is a first step towards developing a methodology to automatically transcribe dynamic markings from vocal rock and pop performances. To this end, we make use of commercial recordings of some popular songs followed by source separation and compare them to the karaoke versions of the same songs. The dynamic variations in the original commercial recordings are found to be structurally very similar to the aligned karaoke/multi-track versions of the same tracks. We compare and show the differences between tracks using statistical analysis, with an eventual goal to use the transcribed markings as guiding tools, to help students adapt with a specific interpretation of a given piece of music. We perform a qualitative analysis of the proposed methodology with the teachers in terms of informativeness and accuracy.

Part of this research is funded by the projects Musical AI (PID2019-111403GB-I00/AEI/10.13039/501100011033 funded by the Spanish Ministerio de Ciencia, Innovación y Universidades (MCIU) and the Agencia Estatal de Investigación (AEI)) and NextCore (RTC2019-007248-7 funded by the Spanish Ministerio de Ciencia, Innovación y Universidades (MCIU) and the Agencia Estatal de Investigación (AEI)).

Comunicació presentada a: The 15th International Symposium on Computer Music Multidisciplinary Research celebrat del 15 al 19 de novembre de 2021 de manera virtual.