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Figure 22. Illustrates the flow of such pitch
converts a pitch contour of singing voice to a
sequence of music notes. The pitch of singing
voice is usually much more unstable than that of
musical instruments.
Furthermore, by adding on the transcription
process, a heuristic music grammar constraints
based on music theory, the error rate can be re-
duced to the lowest.
Beat/tempo tracking
In this section we will describe beat and tempo
tracking contest; to define these contests we want
to cite Simon Dixon (2001):
The task of beat tracking or tempo following is
perhaps best described by analogy to the human
activities of foot-tapping or hand-clapping in time
with music, tasks of which average human listeners
are capable. Despite its apparent intuitiveness and
simplicity compared to the rest of music percep-
tion, beat tracking has remained a difficult task
to define, and still more difficult to implement in
an algorithm or computer program.
These algorithms should be able to estimate the
tempo and the times of musical beats in expres-
sively performed music. The input data may be
either digital audio or a symbolic representation
of music such as MIDI (Zoia, 2004).
This kind of program finds application in tasks
like beat-driven real-time computer graphics,
computer accompaniment of a human performer,
lighting control, and many others. Tempo and
beat tracking are directly implemented in MIR
systems, in fact every song has a distinctive beat
and metronome BPM.
It should be noted that these tasks are not re-
stricted to music with drums; in fact the human
ear can identify beats and tempo even if the song
does not have a strong rhythmical accentuation.
Obviously these tasks are more difficult in music
without drums. The algorithms that implement
tempo and beat tracking actually have less ac-
curacy in music without drums.
include power spectrum analysis, peak grouping,
and harmonic energy estimation.
Wang, Lyu, and Chiang (2003) describe a sing-
ing transcription system, which could he divided
into two modules. One is for the front-end voicing
processing, including voice acquisition, end-point
detection, and pitch tracking, which deal with the
raw singing signal and convert it to a pitch con-
tour. The other is for the melody tracking, which
maps the relatively variation pitch level of human
singing into accurate music notes, represented
as MIDI note number. The overall system block
diagram can be shown as Figure 22.
The melody tracker is based on Adaptive
Round Semitones (ARS) algorithm, which
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