#2013 The Acoustical Society of Japan

Acoust. Sci. & Tech. 34, 6 (2013)

PAPER

Inter-subject diﬀerences in personalized technical ear training
and the inﬂuence of an individually optimized training sequence
Sungyoung Kim1; , Teruaki Kaniwa2; y, Hiroko Terasawa3;4; z,
Takeshi Yamada5; x and Shoji Makino2; }1Electrical, Computer and Telecommunication Engineering Technology,
Rochester Institute of Technology, Rochester, NY, USA
2
TARA Center, University of Tsukuba, Tsukuba, Japan
3
Faculty of Library, Information and Media Science, University of Tsukuba, Tsukuba, Japan
4
JST, PRESTO, Tokyo, Japan
5
Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Japan(Received 13 January 2013, Accepted for publication 20 August 2013)
Abstract: Technical ear training aims to improve the listening of sound engineers so they canskillfully modify and edit the structure of sound. Despite recent increasing interest in listening ability
and subjective evaluation in the ﬁeld of audio- and acoustic-related ﬁelds and the subsequentappearance of various technical ear-training methods, the subject of how to provide eﬃcient training
for a self-trainee has not yet been studied. This paper investigated trainees’ performances and showedthat an (inherent or learned) ability to correctly describe spectral diﬀerences using the terms of aparametric equalizer (center frequency, Q, and gain) was diﬀerent for each person. To cope with suchindividual diﬀerences in spectral identiﬁcation, the authors proposed a novel method that adaptively
controls the training task based on a trainee’s prior performances. In detail, the method estimates the
weakness of the trainee, and generates a training routine that focuses on that weakness. Subsequently,
we tried to determine whether the proposed method—adaptive feedback—helps self-learners improvetheir performance in technical listening that involves identifying spectral diﬀerences. The results
showed that the proposed method could assist trainees in improving their ability to identify diﬀerencesmore eﬀectively than the counterpart group. Together with other features required for eﬀective selftraining, this adaptive feedback would assist a trainee in acquisition of timbre-identiﬁcation ability.
Keywords: Technical listening, Personalized training, Adaptive feedback, Spectral identiﬁcation, Ear
training
PACS number: 43.66.+y1.

[doi:10.1250/ast.34.424]

INTRODUCTION

Technical listening refers to a cognitive process and its
corresponding ability through which a listener can systematically discriminate and identify sonic diﬀerences. In theaudio and music business, technical listening ability is
considered essential for sound processing ‘‘in order tocreate the desired quality of sound,’’ as asserted byMiśkiewicz [1]. It has been common that an assistantengineer learns this ability by observing how an experi

e-mail: sxkiee@rit.edu
e-mail: s0913120@coins.tsukuba.ac.jpze-mail: terasawa@slis.tsukuba.ac.jp
xe-mail: takeshi@cs.tsukuba.ac.jp}
e-mail: maki@tara.tsukuba.ac.jp
y424

enced senior engineer fulﬁlls requests from clients, andprovides them with improved sound quality. While thiskind of on-the-job training has been greatly respected andeﬀective, it is not eﬃcient, as it requires ‘‘a great deal oftime [2].’’ A systematic training curriculum for technicallistening could therefore reduce the time that is required forentry-level engineers to reach an expert level.
Since Retowski’s initial work, Timbre Solfeggio, many
institutions have developed systematic training programsof technical listening, often referred to as technical ear
training, for their junior employees or students [1–8].
Whereas the speciﬁc purpose of the training program varies
according to educational goals, the fundamental training
method is to have trainees compare a reference signal with
its sonically modiﬁed version, comprehend the diﬀerence,

S. KIM et al.: INFLUENCE OF INDIVIDUALIZED TRAINING

and then repeat the process until they can reliably identify
the sonic diﬀerence without a reference. This is similar
to a general learning process. For example, Iwamiya et al.
[2] compared this technical ear-training to ‘‘the simulation
training which astronauts undergo.’’ Just as astronauts canhandle unexpected situations in space through numerous
simulated missions and operational tasks, a technical eartraining program aims for a trainee to acquire fundamental
principles of critical listening to ‘‘face the real job
contexts’’ [2] at any time.
Traditionally, technical ear training was conducted in
private or group sessions and required a dedicatedinstructor. In private training (such as [4]), an instructor
provides the trainee with demonstrative work and thenecessary feedback for eﬃcient learning. While this private
training is an ideal method, it is usually reserved for small
numbers of trainees. Therefore, many institutions oﬀer
their students group training. The beneﬁt of group trainingis that trainees can share the listening experience anddevelop common descriptors corresponding to physicalvariations of the auditory signals. Both students [2] andcompany employees [9] who have received group training
have reported that the commonly developed descriptors
helped them to communicate about acoustics- and audiorelated topics in an eﬀective and consistent way, thus
reducing the chances of misunderstanding others. Anothermerit of group training is that the instructor can motivate
trainees by stimulating soft competition among them.
Marui [10] found that students, especially those who havejust begun ear training, have a tendency to become more
active when they notice the progress of their colleagues.
The beneﬁts from the private and group training are noteasily shared by individuals who are interested in thetechnical ear training yet have no instructor who can guidethem. Individuals in this situation have to train themselves,using teaching materials available in various forms suchas audio CDs [3], software [11,12], software with tutorial
book [7], and an iPhone application [8]. This training canbe categorized as self-training.
However, a self-trainee often faces diﬃculties incontinuing the training. One problem is that self-training
materials often do not evaluate the performance and informthe trainees about their progress. This could disincentivize
the trainees, causing passive and unmotivated training.
Another problem is that self-training material does notinclude appropriate feedback to guide the trainee to achieveeﬀective learning. For example, if a trainee tends to repeat
easy training that he or she seems to learn well enough,it would be necessary for the training material to judge
whether the trainee should move on to new and morechallenging material. Also, if one is struggling to learn a
speciﬁc ability, a learning program should guide the trainee
to repeatedly practice the related task to acquire the ability.

A potential solution to assist self-trainees is to develop a
new training system that adaptively interacts with a trainee,
which is common in e-learning and game-based learning.
Garris et al. [13] reported that game-based learning can beeﬀective because of ‘‘the intensity of involvement andengagement that computer games can evoke.’’
The new training system records a trainee’s training
history, analyzes his or her performance, and adaptively
adjusts the presentation of training materials. In otherwords, the trainee can avoid redundant training and obtain
individualized guidance for fast learning, with improvement of poor areas and eﬀective self-study. We proposedthat the new system can function as an instructor who
acts upon ‘‘the available knowledge on its users and thesubject matter at hand, to dynamically facilitate thelearning process’’ [14].
With these goals, we conducted two experimental
studies with two corresponding research questions: (1) Is
there a listener-dependent idiosyncrasy in identifying
spectral diﬀerences? and (2) Do adaptively regenerated
training contents (for a speciﬁc trainee) assist in acquiring
timbre identiﬁcation ability more eﬀectively than nonadaptive ones?
For the two experiments, we developed a personal eartraining program. The remainder of this paper includes atechnical description of the program, experimental methods, results, and discussion.

2.

PERSONAL TIMBRAL EARTRAINER (PTET)

2.1. Operational Features of the Training ProgramThis section describes the operational features of atechnical ear-training program, the Personal Timbral Ear
Trainer (PTET), which was developed for self-trainees.
The program was implemented using a graphics-based
software development language, Max/MSP. As its nameimplies, PTET mainly focuses on technical-listening training associated with discrimination and identiﬁcation of
timbral diﬀerence. This program is based on the Timbral
Ear Training developed at McGill University [4], but it is
modiﬁed for self-training.
There are many tools [2,3,7,8] that provide a traineewith diverse and detailed categories of ear trainings that
include detection and identiﬁcation of timbral, dynamic,
spatial, and temporal modiﬁcation of a sound source. Suchdiversity is important in raising one’s overall listening
ability. We decided to focus mainly on timbral training
because timbre-related attributes inﬂuence overall perceived sound quality of produced or reproduced sound ﬁeldand because appropriate timbre balance is regarded as themost important factor in that perception [15,16].
According to Corey’s book [7], there are two categories
in timbral ear training: matching/removing and identiﬁca-

425

Acoust. Sci. & Tech. 34, 6 (2013)tion. In the matching/removing category, a trainee shapes
the spectrum of the stimulus using parameters of aparametric equalizer [17] (center frequency, gain, and Q)
until he or she makes the spectrum of stimulus equal to thereference. This kind training is similar to the practical workof mixing or sound-reinforcement engineers, who try to
remove spectral peaks or dips of the given sound ﬁeld andmatch it to their ‘‘internal’’ references. Without this internalreference, it would be hard to make the appropriate
adjustment. This leads us to the second training category:
identiﬁcation. The goal of the identiﬁcation training is
to increase a trainee’s ability to identify a small timbral
diﬀerence, link it to the technical parameters, andeventually build a long-term memory using the technicalparameter so that it can be used as an internal reference.
While it is possible to conﬁgure PTET to satisfy bothcategories and it is recommended that an inexperienced
trainee train in both tasks, we set PTET to provide only
identiﬁcation training. The decision was made because
the identiﬁcation training is for the acquisition of a longterm memory and is easier for inexperienced trainees toachieve.
PTET asks trainees to identify a given spectrum
modiﬁed at one of seven center frequencies (125 Hz,
250 Hz, 500 Hz, 1 kHz, 2 kHz, 4 kHz, and 8 kHz) with one
octave boost (peak) or cut (dip). For each question, a
trainee can compare the non-processed sound at any
moment during reproduction and report the identiﬁed
center frequency. The program keeps reproducing thesound in a loop until the trainee provides the answer, andafter the trainee conﬁrms the frequency, the program
displays the correct answer. Each training session is
conﬁgured to have 25 identiﬁcations. This number was
carefully chosen after heuristic trials conducted by theauthors so that a single session can be eﬀectively
completed while maintaining a trainee’s concentration.
PTET lets a trainee conduct training with four soundsources: pink noise, orchestra, solo piano, and drums. We
selected a portion of each sound ﬁle that contained all of
the target bands required for the training, and the durationsof the sound ﬁles were 1 min (pink noise), 1 min 37 s(orchestra), 1 min 39 s (solo piano), and 54 s (drums),
respectively. The program also allowed trainees to selecttheir own sound ﬁles (as a standard wave-ﬁle format) to be
used for training purposes.
2.2. Adaptive Features of PTET
As explained in the Introduction section, the research
objective of this paper was to experimentally investigate
whether an adaptive training method would assist ineﬀective learning by self-trainees. For this purpose, we
manipulated PTET to operate in two modes: conventional
and adaptive, as illustrated in Fig. 1. In a conventional

426

Fig. 1 The operational modes and associated block
diagrams of PTET. The upper ﬁgure indicates that
PTET uniformly generated questions regardless of
personal performance, while the lower one reﬂects
previous personal training history and the adaptively
adjusted probability of the question appearance.

mode, question-appearance probability is uniformly distributed, while in an adaptive mode a new probability is
calculated according to an individual performance history.
In other words, the adaptive mode generates a newprobability of question appearance by calculating theweighting function of each frequency band for a speciﬁctrainee. It divides the relative weight of a speciﬁcfrequency band by the entire weight as follows:
Pn ¼

Wn
;
W

ð1Þ

where n is the index number of the seven frequency bands,
Pn is the probability of the question appearance for thefrequency band n, and Wn is the relative weight of thefrequency band n.
W is the entire weight, denoted as
W¼

k
X

Wn ;

ð2Þ

n¼1

where k is the maximum number of bands (currently 7).
Wn is calculated as follows:
Wn ¼ 100 

100  L
Rn ;
100

ð3Þ

where L is the minimum weight and Rn is the correct
answer rate for band n. Wn decreases monotonously as Rn
increases. To conﬁrm whether a trainee has achieved a highcorrect rate by chance, the program needs to test even fora high-score band by assigning a non-zero value to L.
Currently, L is set at 25 after several heuristic trials,
resulting in a Wn value range of 25–100.

S. KIM et al.: INFLUENCE OF INDIVIDUALIZED TRAINING

It should be noted that Wn in a conventional mode isalways 100 for any value of n. Nevertheless, a single
session (25 questions) would result in a diﬀerent number ofquestion appearances for frequency bands. We also conﬁrmed that a conventional mode with multiple training
sessions provided a uniformly distributed probability for all
frequency bands.

3.

EXPERIMENT I: INTER-SUBJECTDIFFERENCES IN IDENTIFYING
SPECTRAL MODIFICATION

3.1. Methods
With the initial version of PTET, we evaluated thefunctional features and possible improvements of the
program in order to make it better serve self-trainees. Fourtrainees participated in the ﬁrst experiment. They wereexperienced listeners who had worked as audio signalprocessing engineers and/or researchers for more than 10
years. Yet, the subjects were untrained listeners for thisexperiment in that they had no previous experience withtechnical ear training. The trainees regularly gathered oncea week and practiced the week’s task as a group under thesupervision of the ﬁrst author, who served as an instructor
and explained the operational features of PTET and themission of the week. The author did not provide feedbackon any trainee’s performance but rather observed whether
there were speciﬁc tendencies in the trainees to identify
spectral diﬀerences. All of the trainees had to complete
each week’s assignment before the following week’s grouppractice. The training continued for eight weeks, and thetraining contents are listed in Table 1.
This curriculum was set to provide eight sessions. It
started with a demonstrative session that was modiﬁedwith a large spectral peak (+12 dB), then it generated
questions of a smaller peak (+6 dB) and provided spectral
dip (12 dB) later. At the end of the training, the trainees
were asked to identify random gain variations (either+12 dB, +6 dB, or 12 dB). During this training, thetrainees were encouraged to evaluate the technical andoperational features of the program and to share theirevaluations with other group members. During this training, the trainees were encouraged to evaluate the technicaland operational features of the program and to share theirevaluations with other group members.
The participants conducted an hour-long group training
session every week with an instructor (the ﬁrst author)
using a loudspeaker (Yamaha MSP-5), and they wererequired to conduct individual practice and assignments
using a set of headphones (Beyerdynamic DT770).
After the eight weeks of training (group and individual), the trainees had to take a test that determined how
consistently they remembered the spectral diﬀerences interms of center frequency and gain. As stated in the

Table 1 Curriculum of eight-week training program.
The stimuli were pink noise and three musical soundsources (orchestra, drum, and piano).
Week

Training Content

1
2
3
4
5
6
7
8

12 dB peak (pink noise)
12 dB peak (musical sources)
6 dB peak (pink noise)
6 dB peak (musical sources)
12 dB dip (pink noise)
12 dB dip (musical sources)
12 dB or 6 dB peak, or 12 dB dip (pink noise)
12 dB or 6 dB peak, or 12 dB dip (musical sources)

Introduction section, the purpose of this test was toexperimentally observe whether there is a listener-dependent idiosyncrasy in identifying spectral diﬀerences. The
test was conducted in a recording studio using the same
loudspeaker that was used for group training (Yamaha
MSP-5). Since all trainees had to participate in the test
simultaneously, they could not hear acoustically identicalsounds. Yet acoustical diﬀerences associated with listener
position were small enough that they did not generatecharacteristic idiosyncrasies that aﬀected the testing results. We used four sound sources—pink noise, orchestra,
drum, and piano—for the ﬁnal test. For each sound source,
we provided trainees with a set of 25 questions. Within aset, three gains (+9 dB, +3 dB, and 12 dB, representing
a large peak, small peak, and dip) were randomly selectedwith equal probability. The +12-dB peak was excluded
because it was too easy for the trainees, and the +3-dB
peak was added as an anchor question to show whether the
trainees could identify a smaller gain change.
3.2. Results
Figure 2 shows the results of the ﬁnal tests. The
ordinate represents the trainees’ correct answer rates
averaged over the four sound sources, while the abscissa
represents the center frequencies of the seven bands. The
test results highlight that the post-training ability to identify
the given spectral modiﬁcation was not the same for all
trainees. This ability is a combination of inherent potential
and learning through training. Even after the eight-weekprogram, one trainee found it diﬃcult to identify spectral
diﬀerences in low-frequency bands, while another had
similar confusion with high frequencies. In addition, thetrainees commented that they felt that identifying the bandthat they already knew seemed ineﬀective and redundant,
and they wanted to get training on the bands that werediﬃcult for them.
The results indicate that the training would be more
eﬀective if it could provide trainees with more exercises in
the areas where their identiﬁcation performance is incon-427

Acoust. Sci. & Tech. 34, 6 (2013)
100
Sub1
Sub2

Correct Answer Rate (%)

90
80
70
60
50
40
30
20
10
0

125

250

500

1k

2k

4k

8k

Center Frequency
Fig. 2 Correct answer rates of the ﬁnal test after theeight-week training program for two trainees, denoted
as Sub1 and Sub2. The rates were averaged over thefour sound sources. While the results of Sub1 showed
lower correct rates in 125 Hz and 250 Hz (lowfrequencies), Sub2 showed lower rates in 2 kHz and
4 kHz (mid-high frequencies).

sistent. For example, trainee Sub2 would beneﬁt from
more training on the frequency band at 4 kHz to build aconsistent internal reference to that spectral change,
whereas for trainee Sub1, training with more weights ona low frequency would be helpful.
Previously, Quesnel [18] observed from his study that‘‘all students’ scores improved and the amount of improvement varied much between student.’’ We also conﬁrmed
the individual dependency and further showed that suchdependency could be associated with individual identiﬁcation ability for each speciﬁc spectral band.

4.

EXPERIMENT II: INFLUENCE
OF ADAPTIVE TRAINING

4.1. Methods
After conﬁrming that individual performance diﬀered
among trainees, we conducted an experiment that investigated whether such individual diﬀerences could be
supported by an adaptive training sequence based on an
analysis of each trainee’s previous performance [19]. This
study involved two trainee groups of four subjects that had
similar descriptive statistics (average correct answer rates(64% and 63%) and variances (4.89 and 5.19)). Subsequently, we conducted a two-tailed t-test, which conﬁrmedthat there were no statistically signiﬁcant diﬀerences
between the two groups (p ¼ 0:63, d f ¼ 6).
Two groups were labeled the Conventional Group,
which was given training with a conventional method, and
the Proposal Group, which was trained with the adaptive
training method. The operational diﬀerence in the twogroups was previously illustrated in Fig. 1, and the program
generates a list of new probabilities of question appearances for a subject in the Proposal Group. For example,
Table 2 shows a list of new probabilities for a subject(Sub2 in Experiment I), which was adaptively modiﬁedusing the correct answer rates in Fig. 2. The process was
also dynamic, so that if the correct answer rate changed in
the middle of a training session the program automaticallyadjusted the question appearance accordingly.
This experiment was controlled so that subjects wererequired to attend weekly training sessions. The experiment
took place in a recording studio at the University ofTsukuba using three types of sound sources—pink noise,orchestra, and piano—during a period of four weeks. We
chose these three representative sound sources based on the
results of Experiment I. While it would have been ideal tobe able to conduct the same curriculum as Experiment I for
the sake of comparison, the Experiment II was performed
with students who were only able to participate for alimited time. Thus, the curriculum was reduced by half
for the four weeks and, each week’s training combined
pink noise and two musical sources.
Subjects were asked to conducted two 30-minutes
practice sessions and to complete the main assignment of
the week. For this individual practice and training, subjects
used a pair of headphones (Sennheiser HD 650).
4.2. Results
Figure 3 shows the results of Experiment II. The
correct answer rates for the three stimuli (averaged overall subjects’ responses to seven frequency bands and fourweeks of training) of the Proposal Group were higher thanthose of the Conventional Group. This trend was the samefor the subsequent analysis of individual frequency bands.
However, as Table 3 shows, the results of a one-sided t-test(signiﬁcance level  ¼ 0:05, where the Bonferroni-adjusted signiﬁcance level is 0.016) indicate that the Proposal

Table 2 Adjusted probability of question appearances based on the correct answer rates of subject Sub2 in Fig. 2 for sevenoctave bands. The initial probability was 14.3% (100/7) for all seven bands. Based on this adjusted-probability table, theprogram generated more questions for frequency bands where Sub2 performed poorly (for example, in the areas of 4 kHz).
Center Frequency

125 Hz250 Hz500 Hz

1 kHz

2 kHz

4 kHz

8 kHz

Correct Answer Rate
Adjusted Probability

75%
9.5%

54.5%
12.8%

63.6%
11.3%

50%
13.5%

8.3%
20.3%

0%
21.7%

66.7%
10.8%

428

S. KIM et al.: INFLUENCE OF INDIVIDUALIZED TRAINING
35
Conventional
ProposalConventional
Proposal

70

30

Number of Occurrences

Average Correct Answer Rate (%)

80

60
50
40
30
20

20
15
10
5

10
0

25

Pink noise

Orchestra

0

Piano

Fig. 3 The average correct answer rates and corresponding 95% conﬁdence intervals of the two groups(averaged over seven center frequencies and fourweeks of trainings). There is a trend that the Proposal
Group has consistently higher rates for all three sounds
than the Conventional Group.

Table 3 The t-test results of evaluating the diﬀerence
between the Proposal Group and the Conventional
Group with the signiﬁcance level  ¼ 0:05 (withthe Bonferroni-adjusted signiﬁcance level 0.016).
Listeners’ correct-answer rates are signiﬁcantly diﬀerent with the piano stimulus.
Sound

Pink Noise

Orchestra

Piano

p

0.11

0.018

0.00038

Group and the Conventional Group became signiﬁcantly
diﬀerent with the piano sound but not with the pink noise
and the orchestra.
In addition, we investigated the improvement on the
low-score band in order to verify whether use of the
proposed method increased the correct answer rate in lowscore bands as intended. For every week, we detected thethree frequency bands where each subject scored lowest,
counted the direction of change (i.e., increase, decrease,and no change) in the correct answer rate of those lowscore bands in the following week, and compared thecounts of the Proposal group and Conventional group.
Figure 4 shows the occurrence number of the score change
directions. The indices of change were ‘‘+’’ = increase,‘‘’’ = decrease, and ‘‘0’’ = no change, respectively.
These results contrast the change of decrease andincrease of the two groups. The Conventional Group shows
more counts on the decrease (‘‘’’) and no change (‘‘0’’)cases than the increase case (‘‘+’’), whereas the Proposal
Group shows more evenly distributed counts for the threecases. This observation implies the possibility that the
Proposal Group did not decrease the score as much as the:decrease

0:no change+:increase

Fig. 4 The change of the correct answer rate of the threelowest-score bands. ‘‘+’’ indicates an increase in thecorrect answer rate; ‘‘’’ represents a decreased correct
answer rate; and ‘‘0’’ indicates no change. The
Proposal Group tended to have a less decreased correct
answer rate and a greater increased correct answer ratecompared with the Conventional Group.

Conventional Group did, even though the training task
became more diﬃcult every week and thus naturally tended
to generate fewer correct answers.
However, the results of the sign-test in the low-score
bands provided no statistically signiﬁcant evidence on thispossibility—the null hypothesis for this sign test was‘‘there was no score decrease,’’ whereas the alternative
hypothesis was ‘‘there was score decrease.’’ The results ofthe sign test (small samples, single-sided, signiﬁcance level ¼ 0:05) were p ¼ 0:06 for the Conventional Group, and
p ¼ 0:77 for the Proposal Group, with both not rejecting
the null hypothesis, and therefore not showing a statistically signiﬁcant contrast between the two groups. Although
it is expected that the correct answer rate in the low-score
bands would be improved by prolonging training time,further investigation on this eﬀect remains for our future
work.

5.

DISCUSSION

Ear training in the audio and acoustic ﬁelds is still very
young and growing, and thus the methodology has not beenstandardized among various programs and institutions. For
example, the training program used in this paper presents
a reference sound and a modiﬁed sound in parallel andallows a trainee to toggle between the two at any point. In
contrast, other programs (including [2,3]) present two
sequential sound sources. This sequential presentation of
stimuli forces trainees to hold their impression of the ﬁrst
sound source and use it as a reference for the second soundsource. While the latter requires more cognitive activity
and thus seems to be more eﬃcient in acquiring listening
ability, the former is much more practical (for audio

429

Acoust. Sci. & Tech. 34, 6 (2013)engineers in particular) as they usually compare a modiﬁed
sound source with a non-modiﬁed (often referred to as a‘‘bypass’’) version when applying a ﬁlter in-situ. Anotherexample is the number of questions in a single training
session. In our program, we have 25, which we arrived
at during program development after considering training
performance and listener concentration. This speciﬁc
number will probably be diﬀerent for other programs.
Likewise, most other parameters have been optimized
through heuristic trial-and-error, and therefore they areprogram dependent, which makes it diﬃcult to establish
standardized training for multiple institutions.
While this program was originally developed to replacea human instructor, it is still in its early stages, and asubstantial amount of work will be required to transformthe program into a smart and instructor-free ear trainer.
Also, in order to fully validate the eﬀectiveness of the
proposed adaptive adjustment in the probability of question
appearances, it will be necessary to conduct a validating
experiment that compares three large groups of trainees: a
self-training group without adaptive adjustment, a groupwith the proposed adaptive adjustment, and a group with ahuman instructor who would analyze and provide individual guidance for the trainees.

6.

CONCLUSION

To support self-learning for identifying spectral modiﬁcation, we developed a personal timbre-training program.
The program is designed to provide trainees with multiple
tasks of increasing diﬃculty along with adaptive feedback
that produces more training with regard to each trainee’s
weak points. The results showed that listeners tend toobtain spectral identiﬁcation for speciﬁc frequency bands
ﬁrst, which made it necessary to design a new training
method that adaptively adjusts the test content for speciﬁc
listeners considering their previous performance. The
consequent study results showed that the personalized
and adaptive feedback could assist trainees in conducting
more eﬀective training by themselves as the diﬃculty of
training increases.
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Sungyoung Kim received a B.S. degree from Sogang University,
Korea, in 1996, and a Master of Music and Ph.D. degree from McGill
University, Canada, in 2006 and 2009 respectively. His professional
work experiences include recording/balance engineer at Korea
Broadcasting System (KBS), Seoul, Korea (1995–2001) and research
associate at Yamaha Corporation (2007–2012), Hamamatsu, Japan.
He is now an assistant professor at Electrical, Computer, andTelecommunication Engineering Department, Rochester Institute ofTechnology. His research interests are spatial audio and humanperception, and eﬃcient ear training method.
Teruaki Kaniwa is a graduate student of University of Tsukuba,
Japan and he has been studying computer science and audio
information processing with Shoji Makino at the Multimedia
Laboratory. He researches the soniﬁcation of EEG steady state
response now.
Hiroko Terasawa received B.E. and M.E. degrees in ElectricalEngineering from the University of Electro-Communications, Japan,

S. KIM et al.: INFLUENCE OF INDIVIDUALIZED TRAINING
and M.A. and Ph.D. degrees in Music from Center for Computer
Research in Music and Acoustics (CCRMA), Stanford University,
USA. Her research interests include timbre perception modeling andtimbre-based data soniﬁcation. She currently serves as an assistantprofessor at University of Tsukuba.
Takeshi Yamada was born in Osaka, Japan. He received theB. Eng. degree from Osaka City University, Japan, in 1994, andthe M. Eng. and Dr. Eng. degrees from Nara Institute of Science andTechnology, Japan, in 1996 and 1999, respectively. He is presently an associate professor with Faculty of Engineering, Information and Systems, University of Tsukuba, Japan. His researchinterests include speech recognition, sound scene understanding,multi-channel signal processing, media quality assessment, ande-learning. He is a member of the IEEE, the IEICE, the IPSJ, and the
ASJ.
Shoji Makino received B.E., M.E., and Ph.D. degrees fromTohoku University, Japan, in 1979, 1981, and 1993, respectively.
He joined NTT in 1981. He is now a Professor at University ofTsukuba. His research interests include adaptive ﬁltering technologies, the realization of acoustic echo cancellation, blind source
separation of convolutive mixtures of speech, and acoustic signal
processing for speech and audio applications.

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