Log In Sign Up

A Geometric Framework for Pitch Estimation on Acoustic Musical Signals

by   Tom Goodman, et al.

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, whilst 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 a theoretical and experimental perspective, we present a novel framework, a basis for further work in the area, and results that (whilst 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.


page 18

page 23

page 24

page 25

page 27

page 28

page 29


Maths, Computation and Flamenco: overview and challenges

Flamenco is a rich performance-oriented art music genre from Southern Sp...

Musical Information Extraction from the Singing Voice

Music information retrieval is currently an active research area that ad...

Machine listening intelligence

This manifesto paper will introduce machine listening intelligence, an i...

Leveraging the structure of musical preference in content-aware music recommendation

State-of-the-art music recommendation systems are based on collaborative...

Deep Music Information Dynamics

Music comprises of a set of complex simultaneous events organized in tim...

Multi-Channel Automatic Music Transcription Using Tensor Algebra

Music is an art, perceived in unique ways by every listener, coming from...