A geometric framework for pitch estimation on acoustic musical signals

Abstract

This paper presents a geometric approach to pitch estimation (PE) -an important problem in music information retrieval (MIR), and a precursor to a variety of other problems in the field. Though there exist a number of highly accurate methods, both mono-pitch estimation and multi-pitch estimation (particularly with unspecified polyphonic timbre) prove computationally and conceptually challenging. A number of current techniques, while incredibly effective, are not targeted towards eliciting the underlying mathematical structures that underpin the complex musical patterns exhibited by acoustic musical signals. Tackling the approach from both theoretical and experimental perspectives, we present a novel framework, a basis for further work in the area, and results that (while not state of the art) demonstrate relative efficacy. The framework presented in this paper opens up a completely new way to tackle PE problems and may have uses both in traditional analytical approaches as well as in the emerging machine learning (ML) methods that currently dominate the literature.

An efficient phonation-driven control system using laryngeal bioimpedance and machine learning

“…Most of the recent literature, however, relies on machine learning and neural networks (Goodman, Gemst and Tiňo, 2021). Whereas such methodology could be highly accurate and effective for static analysis, employing a neural network could cause the pitch estimation system to be too slow for real-time usage.…”