The digital music archive has been enriched with descriptions concerning the musical parameters tempo and pitch. Music Information Retrieval (MIR) techniques have been applied: computer analysis methods that extract musical information from digital audio. The used techniques have been tested extensively and were evaluated positively, but since all the data have been created by automated processes and could not be checked individually because of the size of the archive, the results of the MIR are not infallible.
The tempo analysis has been performed by the software
BeatRoot,
by Simon Dixon. This software suggests timestamps on which a 'beat' in
the music occurs. From this list of temporal data, the actual tempo
(and related temporal information) could be derived. Also a graph
was made for each song, visualising the tempo development of the
whole piece at first glance.
In general, but also in this case, there must be remarked that tempo annotation can hold a degree of
ambiguity: research pointed out that there are plural temporal
levels within one piece of music. Manual annotation of tempo
enforced this hypothesis of ambiguity. Listeners can attribute
several tempi to a piece of music. These tempi are mostly related
to each other. For example are the tempi 60 BPM (beats per minute)
and 120 BPM closely related. The polyrhythmic character of some
African music enforces the ambiguity of tempo annotation, pointing
towards triple, quadruple and quintuple relations. Tempo annotation
by software faces the same problem: only one tempo can be
attributed and this does not necessarily match the tempo of an
individual (or your) tempo annotation. Keep in mind that another
tempo than expected, can indeed be a correct, though other
levelled, tempo! The most often occurring relationship is the
halving and doubling of the tempo.
This possible ambiguity aside,
we can say that the BeatRoot gives an accurate annotation of the
tempo. Random chosen songs were annotated by BeatRoot and its
results were compared with manual annotations made by a group of
ten people. 67% of the tempi were annotated identically, 26% were
annotated in a related tempo and 7% were assigned a non-relating
(wrong?) tempo.
The BeatRoot assigns tempi between 60 and 200BPM.
From the BeatRoot annotations several temporal characteristics
could be derived: tempo (average and median), standard deviation
(deviation of general tempo), minimal and maximal tempo, regression
(tempo development, percentage of acceleration/deceleration of the
beginning tempo). On the website, four can be consulted: average
and mean tempo, standard deviation and regression.
For every song, there is a graph visualising the tempo development in time:
X-axis represents the time (in sec), while Y-axis shows the tempo
(BPM). On top of these annotations, a fitting curve has been
calculated, showing the regression (green line). The regression is
a value above or below zero. A negative value implies that the song
in its totality has decelerating tempo, while a positive value
indicates a global acceleration. The regression value is the
percentage that the tempo increases/decreases per second (average
of the entire song). The right side of the graph shows a histogram
clustering how many times the tempi did occur. The width of all
these annotations makes the standard deviation: a small value
implies a stable tempo while a larger value means more rubato or
changing tempo.
The 5 graphs visualise characteristics of the recording that are hard to express with
semantic terminology. Sometimes the Western musical concepts lack,
sometimes varying elements over time are easier to visualise in a
graph than in words. The added value for sure is obvious!
May it stimulate further research!