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  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>An Exploratory Data Analysis of the Ability of Western Musicians to Cope with Latency</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Jorge Medina Victoria</string-name>
          <email>jorge.medina.victoria@h-da.de</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Cork Institute of Technology</institution>
          ,
          <addr-line>Cork</addr-line>
          ,
          <country country="IE">Ireland</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>Darmstadt University of Applied Sciences</institution>
          ,
          <addr-line>Darmstadt</addr-line>
          ,
          <country country="DE">Germany</country>
        </aff>
      </contrib-group>
      <fpage>37</fpage>
      <lpage>47</lpage>
      <abstract>
        <p>Technology based on networks has long ago become state of the art for music recording, concert venues and even as a medium for collaborative performances over the Internet. However, there is a drawback attached to this technology. Latency is a permanent issue associated to any network technology. On the other hand, musicianship relies on time perception. The performance of music and playing an instrument are activities highly affected by latency. The present paper describes a methodology in order to measure how latency and the performed western musical instruments are related. In addition, the latency tolerance range (LTR) is presented as a comparison measurement between the different musical instrument groups. The results are based on the exploratory data analysis of the gathered data.</p>
      </abstract>
      <kwd-group>
        <kwd>Latency</kwd>
        <kwd>Latency Tolerance Range</kwd>
        <kwd>Tempo</kwd>
        <kwd>Musical Instrument Groups</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>Introduction</title>
      <p>
        Research on the issue of latency and musicianship is focused on timing and
the improvement of network technical conditions, in order to enable
collaborative performances [
        <xref ref-type="bibr" rid="ref3 ref4 ref7">7, 3, 4</xref>
        ]. This paper presents the exploratory data analysis
of empirical observations of solo-playing musicians performances known as
noncollaborative performances. To gather the data, a methodology was developed
using a listening test setup and a questionnaire. The exploratory analysis and the
numerical results may enhance the knowledge framework regarding the latency
issue and the performance of music, enabling comparisons between different
musical instrument groups. Beyond a further understanding of musicianship and
the relation between performer and musical instrument, the outcome delivers
important information for the design of virtual musical instruments where
issues, such as haptics, virtual acoustics and immersion play a primary role.
2
2
      </p>
    </sec>
    <sec id="sec-2">
      <title>Methodology</title>
      <p>
        A total of 31 test subjects playing 17 different western musical instruments took
part in a listening test and answered a questionnaire. Every musician played
only his or her main musical instrument. The different musical instruments were
divided into the groups of aerophones, chordophones, membranophones and
idiophones based on the Hornbostel-Sachs taxonomy [
        <xref ref-type="bibr" rid="ref8">8</xref>
        ].
      </p>
      <p>
        The setup of the listening test is described in [
        <xref ref-type="bibr" rid="ref6">6</xref>
        ] and consisted of an audio
interface, a cardioid pattern microphone, headphones, a laptop with a digital
audio workstation (DAW) and a 7-inch monitor as shown in Figure 1. The
musician played his/her instrument, the audio signal was recorded and sent back
delayed (latency was introduced progressively) to the musician’s headphones,
until the test subject was not able to play any more and disrupted the
performance. Three different metronomes (aural, visual and aural-visual) were used as
a control mechanism. In addition, a predefined score was the same for every test
subject. The notes of the score defined a specific pitch and it was used to enable
equal conditions to all participants while playing the musical instrument,
comparisons between results are possible. Some membranophones and idiophones
have no definite pitch. However, variations on the timbre of all different
instruments are expected due to the spectrum and envelope characteristics. Having
more than one frequency component traduces in a vibration response in different
places along the inner ear [
        <xref ref-type="bibr" rid="ref9">9</xref>
        ]. The impact of the musical instrument frequencies
with relation to the latency issue is irrelevant at this level.
      </p>
      <p>Every one of the 31 musicians played the music score five different times
according to the tempi 90 BPM, 120 BPM, 150 BPM, 180 BPM and 210 BPM,
using a different metronome each time. The three metronomes were regulated
using the MIDI protocol and have not been delayed. At the end of the listening
test, 15 different latency values were gathered (5 tempi and 3 metronomes).</p>
      <p>It is possible to define the total latency of the system used in the listening
test by means of the following mathematical expression:</p>
      <p>Lt = La + Lc + Ld
(1)</p>
      <p>Lt is the total latency, which is usually measured in milliseconds. The total
latency is the sum of the latency due to the sound transmission La, the latency
produced by the analog (sound waves) to digital (information in the DAW) to
analog (sound waves to the headphones) conversion Lc and Ld which is the
network latency simulated in the listening test.</p>
      <p>The latencies La and Lc can be considered constants. The value Lc was
measured and was the same for every listening test (12.208ms). On the other hand,
the value of La is directly related to the distance of the microphone to the
instrument as presented in Table 1. Prior to the beginning of each listening test,
the distance microphone to instrument and the sound pressure level (SPL) of the
musical instrument were measured, in order to guarantee equality of conditions.
In addition to those measurements, the level gain in dB of the microphone and
the headphones, as well as the reverberation time (RT60 in seconds) and noise
level of the different rooms were measured. The information presented in Table
1 summarizes the range, median, mean, variance and standard deviation for the
31 test subjects (nbr.val).</p>
      <p>Based on the information presented in Table 1 it is clear that all of the
instruments were recorded under similar conditions. Furthermore, the results of the
first column have a very low variance, which means that the distance between
microphone and instrument was almost the same. In other words, La, which is
the latency produced due to sound transmission, can be assumed as a constant.
4</p>
      <p>The value Ld, which is the simulated network latency, is the numerical
outcome of the listening test. As stated before, musicians played the score while
listening to their own delayed3 signal (latency was introduced). The score
consisted of two bars and was played in a loop. Every second repetition (4 bars), the
delay was increased by 10ms. The latency range Ld variated from 0 to 300ms.
At 0ms only La and Lc were present and imperceptible for the majority of
musicians. The latency value Ld was obtained by a performance disruption, when
the musician interrupted the performance the value Ld was notated.
3</p>
    </sec>
    <sec id="sec-3">
      <title>Exploratory Data Analysis</title>
      <p>After having collected the different latency values Ld, in addition to further
numerical information of the questionnaire such as age, hours of instrument practice
with and without metronome and years of experience on the instrument, it is
possible to visualize the information in a scatter plot as presented in Figure 2.
3 The concepts of latency and delay are the same for this work.</p>
      <p>In Figure 2, every numerical variable is plotted against each other. The
variables are written on the diagonal and all relationships above and under the
diagonal are mirrored. The numerical scale for age ranges from 0 to 50 years, however
mainly young European musicians took part in the research. The scale for the
years of experience ranges from 0 to 25 years, practice hours with metronome
from 0 to 8 hours and without (practice hours) from 0 to 30 hours. Finally all
latency values (Ld) distributed according to the five different tempi (90 BPM to
210 BPM) have the range 0 to 300 milliseconds.</p>
      <p>The linear trends in Figure 2 are easy to identify for columns five to nine,
which are related to the latency values Ld. Additionally, another linear
relationship is present between age and years of experience (column 3, row 1) which is
obvious. The older a musician is, the more years of experience he or she has.
On the other hand, it is interesting that no visual relationships exists between
the years of experience, musical practice with and without metronome and the
latency values Ld for the different tempi. It may indicate that those variables
have no meaningful role at all, regarding the ability to cope with latency.</p>
      <p>A further procedure is to visually analyse the shape of the distribution.
Figure 3 presents the explorative data analysis (EDA) for the distribution of the
gathered data.
6</p>
      <p>Medina Victoria, Jorge</p>
      <p>From the raw data plot in the upper left of Figure 3, two details are clear.
Firstly, the data has not a random distribution and secondly, the data shows a
decaying behaviour. The next plot on the right is a histogram, additional to the
observation regarding a non-normal distribution. It is also possible to see the
outliers (data in the 300 bar). Finally, the lower right plot (Q-Q plot) confirms
that the data is non-normally distributed. The confidence intervals (red dashed
lines) are clearly overpassed.</p>
      <p>
        Table 2 lists all musical instruments that have been analysed. The selection
of test subjects was not completely random. This purposive sample represents a
virtual population and constitute a random sample [
        <xref ref-type="bibr" rid="ref2">2</xref>
        ].
Subject Instrument Group Subject Instrument Group
1 Piano Chordophones 17 Trombone Aerophones
2 Piano Chordophones 18 Violin Chordophones
3 Cello Chordophones 19 Triangle Idiophones
4 Cello Chordophones 20 Tenor saxophone Aerophones
5 Classical guitar Chordophones 21 Classical guitar Chordophones
6 French horn Aerophones 22 Violin Chordophones
7 Alto saxophone Aerophones 23 Snare drum Membranophones
8 Violin Chordophones 24 Alto saxophone Aerophones
9 Trumpet in B Aerophones 25 Triangle Idiophones
10 Snare drum Membranophones 26 Marimba Idiophones
11 Piano (upright) Chordophones 27 French horn Aerophones
12 Snare drum Membranophones 28 Double bass Chordophones
13 Violin Chordophones 29 Harp Chordophones
14 Transverse flute Aerophones 30 Bassoon Aerophones
15 Trombone Aerophones 31 Tenor saxophone Aerophones
16 Timpani Membranophones
      </p>
      <p>
        In Figure 4, data from the three different metronomes (aural, visual and
aural-visual) is presented and data from similar instruments, e.g. piano (1),
piano (2) and piano upright (11) have been averaged. Figure 4 represents the
latency value (Ld) vs. tempo in BPM. This relationship is known from
Barbosa [
        <xref ref-type="bibr" rid="ref1">1</xref>
        ] as the latency adaptive tempo (LAT).
      </p>
      <p>It can be noticed that there are very few differences between the results of
the different metronomes (except for instruments such as marimba, bassoon and
alto saxophone), therefore, it may be possible to average the latency results Ld
of the three different metronomes. Observing Figure 4, it is clear that choosing
a western musical instrument has an impact regarding the ability to cope with
latency. For example, a musician playing the snare drum tends to interrupt the
performance earlier (lower latency value Ld) compared to a cello player.</p>
      <p>
        The data of all musical instruments, according to the different five tempi of
the experiment could be presented using the kernel density estimation (KDE).
The KDE is a smoothed version of the histogram [
        <xref ref-type="bibr" rid="ref5">5</xref>
        ] and determines the shape
of distribution. In Figure 5 the kernel density estimation for a continuous value
Ld is presented.
      </p>
      <p>Medina Victoria, Jorge</p>
      <p>It is important to state that the listening test delivered only five discrete
latency values Ld, according to every tempi and to every metronome, as seen
in Figure 4. For the Kernel density estimation (KDE) in Figure 5, the values of
every metronome (aural, visual and aural visual) were averaged.</p>
      <p>Figure 5 clearly shows the peaks for the latency values Ld. The higher the
tempo (210 BPM and 180 BPM) the narrower the peak. It indicates that by
playing at higher tempi, all musicians stop the performance at approximately
the same latency value Ld. For the tempi 90 BPM, 120 BPM and 150 BPM
there is no observable narrow peak. On the contrary, for some tempi (90 BPM)
there are more than one peak and different values of Ld present. It might suggest
that different musical instruments have dissimilar latency values Ld, where the
performance is interrupted. The lower peaks at 300ms (right side of Figure 5) are
produced by those musicians who were able to play even at the higher latency
values (Ld) up to 300ms (e.g. trumpet in B). Those values of Ld can be assumed
as outliers.
3.1</p>
      <p>Latency Tolerance Range
The measure of the latency tolerance range (LTR) is the estimation of a range
in milliseconds where latency is tolerable according to the different musical
instrument groups. The (LTR) can be defined as the difference between the third
and first quartile of the gathered data and is better suited than the mean or
median to describe non-normal distributions.</p>
      <p>The latency tolerance range (LTR) is measured in milliseconds and its
mathematical expression is:
(2)</p>
      <p>The latency tolerance range is dependant on the amount of data gathered.
However, the tendency is clear. Playing a membranophone may be a difficult
task regarding latency. Aerophones, chordophones and idiophones may perform
music as an ensemble within similar latency ranges, without any inconvenience.
4</p>
    </sec>
    <sec id="sec-4">
      <title>Discussion and Conclusions</title>
      <p>The present paper presented the exploratory data analysis of a controlled
listening test. The research design and test characteristics such as the use of a
10</p>
      <p>Medina Victoria, Jorge
metronome and a score enabled comparisons of the results. Based only on the
exploratory data analysis of the gathered data, it is possible to claim that the
ability to cope with latency is affected by the musical instrument played.
Performing on different musical instruments may have an effect on the ability to
cope with latency. Furthermore, the years of experience playing an instrument,
the hours of practice and even the use of a metronome while practicing may not
affect this ability at all.</p>
      <p>The unbalanced design regarding the number of musical instruments tested
per instruments type and the lower number of test subjects or musicians might
diminish the external validity of the outcomes. However, there are clear
tendencies and patterns and the effect is far from random.</p>
      <p>The influence of the musical instrument, regarding the latency issue,
decreases while performing at faster tempi above 180 BPM. On the contrary, this
influence increases by playing a musical instrument at lower tempi under 150
BPM. In addition, the latency tolerance range (LTR) enable the comparison of
results regarding the issue of latency between the different musical instrument
groups.</p>
      <p>The results and the methodology presented, may constitute a further step
in the modelling of musical instruments for technologies such as virtual reality.
Moreover, based on the results presented, software requirements for artistic and
educational projects, such as network musical live performances and distance
learning programs can be further developed to fit better the requirements
regarding musical instrument groups.</p>
      <p>Ability of Western Musicians to Cope with Latency
11</p>
    </sec>
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