Music Information Retrieval: Examining what we mean by success.

Music Information Retrieval: Examining what we mean by
success.

J.
Stephen
Downie

Graduate School of Library and Information
Science, University of Illinois at
Urbana-Champaign
jdownie@uiuc.edu

2002

University of Tübingen

Tübingen

ALLC/ACH 2002

editor

encoder

Sara
A.
Schmidt

Music information retrieval (MIR) research has been a part of humanities
computing for many years. Kassler (1966) and Lincoln (1967) are both examples of
early work in this area. Over the last several years, there has been a resurgent
interest in MIR research and development (see ISMIR 2000; ISMIR 2001). Inspired,
in part, by the success of the text retrieval research community (e.g.,
Yahoo.com, Google.com, Altavista.com, etc.), present-day MIR researchers strive
to afford the same kind of content-based access to music. Within the context of
music, content-based retrieval implies the use of musical elements to retrieve
music objects or specific parts of music objects: Music-based search inputs
(e.g., humming, singing, notation excerpts, MIDI keyboard, etc.) to search, and
then retrieve, musical works (e.g., recordings, scores, etc.) or to locate
sub-components of musical works (e.g., repeating themes and motifs, harmonic
progressions, etc.).
Notwithstanding the promising developments enumerated in the recent MIR
literature, MIR systems are still far from being as useful, comprehensive, or
robust as their text information retrieval (IR) analogues. There are three
principal reasons for this state of affairs.
First, prompted by educational, governmental, scientific, economic, and military
imperatives, the text IR community has for many years garnered substantial
financial support which as allowed countless person-hours of effort to be spent
on research, development, and evaluation. Until very recently, most MIR research
projects have been undertaken primarily as labours of love by devoted scholars.
A half-hour's perusal of the back issues of Computing in
Musicology (Hewlett and Selfridge-Field, eds.) will bring this fact
to the fore.
Second, music information is inherently more complex than text information. Music
information is a multi-faceted amalgam of pitch, tempo, rhythmic, harmonic,
timbral, textual (i.e., lyrics and librettti), editorial, praxis, and
bibliographic elements. Music can be represented as scores, MIDI files and other
discrete encodings, and in any number of analogue and digital audio formats
(e.g., LPs, tapes, MP3s, CDs, etc.). Unlike most text, music is extremely
plastic; that is, a given piece of music can be transposed, have its rhythms
altered, its harmonies reset, its orchestration recast, its lyrics changed, and
so on, yet somehow it is still perceived to be the same piece of music. This
interaction of music's complexity and plasticity make the selection of possible
retrieval elements extraordinarily difficult.
Third, the text IR community has had a set of standardized performance evaluation
metrics for last four decades. Taking the Cranfield evaluations of the early
1960's (Cleverdon et al., 1966) as the starting point of modern text IR
research, two metrics have to this day continually proved themselves to be
particularly important: precision (i.e., the ratio of relevant documents
retrieved to the number of documents retrieved) and recall (i.e, the ratio of
relevant documents retrieved to the number of relevant documents present in the
system). The key determinant in the use of precision and recall as a performance
metric is the apprehension of those documents deemed "relevant" to a particular
query. While there have been ongoing debates about the nature of "relevance"
(see Schamber, 1994), relevance has had relatively stable meaning across the
text-IR literature. Simply put, a "document" is deemed to be "relevant" to a
given query if the document is "about" the same subject matter as the query.
With these metrics, text IR researchers have been able compare and contrast the
results of many different retrieval approaches. Thus, promising approaches have
been explored more thoroughly, and weaker approaches abandoned. At present,
there are no standardized evaluation metrics for MIR. Because of this lack of
metrics, MIR researchers have had no means of effectively comparing and
contrasting MIR methods. MIR research has not been able to move forward as
quickly as it should because it has had no demonstrable basis for concentrating
its efforts on better techniques nor for abandoning weaker approaches.
My poster examines the reasons behind the lack of standardized performance
metrics for MIR research and development. Its primary focus is on the
suitability of the precision and recall as candidate MIR metrics. Seeing that
precision and recall have been instrumental in the success of text IR research,
this limited focus is justified. The crux of this explication is the exploration
of the nature of "relevance" as it pertains to MIR tasks. The notion of
relevance in the MIR context must undergo considerable scrutiny. Without a
proper understanding of the applicability, limitations, and implications of
relevance, the use of precision and recall as MIR evaluation metrics will not
have the theoretical grounding necessary to justify their use by MIR
researchers.

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