Audio description and corpus analysis of popular music

In the field of sound and music computing, only a handful of studies
are concerned with the pursuit of new musical knowledge. There is
a substantial body of corpus analysis research focused on new musical
insight, but almost all of it deals with symbolic data: scores, chords or
manual annotations. In contrast, and despite the wide availability of
audio data and tools for audio content analysis, very little work has
been done on the corpus analysis of audio data.
This thesis presents a number of contributions to the scientific study
of music, based on audio corpus analysis. We focus on three themes:
audio description, corpus analysis methodology, and the application
of these description and analysis techniques to the study of music
similarity and ‘hooks’.
On the theme of audio description, we first present, in part i, an
overview of the audio description methods that have been proposed
in the music information retrieval literature, focusing on timbre, harmony
and melody. We critically review current practices in terms of
their relevancy to audio corpus analysis. Throughout part ii and iii,
we then propose new feature sets and audio description strategies.
Contributions include the introduction of audio bigram features, pitch
descriptors that can be used for retrieval as well as corpus analysis,
and second-order audio features, which quantify distinctiveness and recurrence
of feature values given a reference corpus.
On the theme of audio corpus analysis methodology, we first situate
corpus analysis in the disciplinary context of music information
retrieval, empirical musicology and music cognition. In part i, we
then present a review of audio corpus analysis, and a case study comparing
two influential corpus-based investigations into the evolution
of popular music [122,175]. Based on this analysis, we formulate a set
of nine recommendations for audio corpus analysis research. In part
ii and iii, we present, alongside the new audio description techniques,
new analysis methods for the study of song sections and within-song
variation in a large corpus. Contributions on this theme include the
first use of a probabilistic graphical model for the analysis of audio
features.
Finally, we apply new audio description and corpus analysis techniques
to address two research problems of the cogitch project of
which our research was a part: improving audio-based models of music
similarity, and the analysis of hooks in popular music. In parts i
and ii, we introduce soft audio fingerprinting, an umbrella MIR task that
includes any efficient audio-based content identification. We then focus
on the problem of scalable cover song detection, and evaluate several
solutions based on audio bigram features. In part iii, we review
the prevailing perspectives on musical catchiness, recognisability and
hooks. We describe Hooked, a game we designed to collect data on
the recognisability of a set of song fragments. We then present a corpus
analysis of hooks, and new findings on what makes music catchy.
Across the three themes above, we present several contributions to
the available methods and technologies for audio description and audio
corpus analysis. Along the way, we present new insights into
choruses, catchiness, recognisability and hooks. By applying the proposed
technologies, following the proposed methods, we show that
rigorous audio corpus analysis is possible and that the technologies
to engage in it are available.