Influences of Signal Processing, Tone Profiles, and Chord Progressions on a Model for Estimating the Musical Key from Audio
Katy C. Noland, Mark B. Sandler · Computer Music Journal · 2009
��� Tonality analysis is an important part of studying music, and in recent years, automatic key estimation from audio input has become an important part of music information retrieval. The primary key is often given in the title of classical music compositions, together with some information about the structure or style, such as Sonata in F or Scherzo in G, which suggests that it is considered by composers to be an important means of classification. This article investigates the effects of three important aspects of many key-estimation algorithms: low-level digital signal processing parameters, tone-profile values, and harmonic progression through time, using a key estimation algorithm based on a Hidden Markov Model (HMM). The terms “key” and “tonality” are often used interchangeably. In this article, we use “key” to refer to a single, discrete tonal center and its associated scale, with the acknowledgment that there may be simultaneous keys present in a given piece of music. We use “tonality” to refer to the more abstract concept of the music’s relationship to all possible keys, which can be modeled as a position within some kind of psychologically informed geometrical tonal space, such as Chew’s Spiral Array (Chew 2001). We begin by explaining the parameters under investigation, and then we describe the technique using HMMs on which we base our investigations. We also explain how the model has been altered to test the importance of DSP parameters, tone profile values, and harmonic progression through time. We provide details of our experiments, which test the algorithm for global key estimation on a set of Beatles songs and a set of preludes and fugues from J. S. Bach’s Well-Tempered Clavier .W e