Proceedings of the 2nd European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies(EWIMT2005), pp.205-212, November 2005.

INTER:D: A DRUM SOUND EQUALIZERFOR CONTROLLING VOLUME AND TIMBRE OF DRUMS
Kazuyoshi Yoshii†

Masataka Goto‡†‡

Department of Intelligence Science and Technology
Graduate School of Informatics, Kyoto University, Japan
yoshii@kuis.kyoto-u.ac.jp

Hiroshi G. Okuno†
National Institute of Advanced Industrial
Science and Technology (AIST), Japanm.goto@aist.go.jp

okuno@i.kyoto-u.ac.jp

Keywords: Interactive music-playback interface, drum soundannotation, drum transcription, music information retrieval.
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Abstract
A drum sound equalizer, called INTER:D, is described thatenables a listener to control the volume and timbre of bass
and snare drum sounds in commercial compact-disc recordings. Although the characteristics of the drum sounds are often
closely related to the impression made by a musical piece, conventional graphic equalizers cannot adjust their characteristics
because they have volume sliders only for the frequency bands.
INTER:D provides drum-specific volume sliders that directly
control the frequency components of drum sounds, enabling a
listener to intuitively cut or boost the volume of each drum.
In addition, INTER:D enables a listener to replace the original timbre of each drum with another timbre selected from a
dropdown list. These interactive functions are achieved using
an automatic music content analysis system based on low-level
audio signal processing. This system can estimate the power
spectrogram of each drum sound and detect the onset times ina musical piece. Subjective experiments showed that INTER:D
can add a new dimension to the way users experience music.

1 Introduction
The development of interactive music-playback interfaces has
recently been facilitated by the application of low-level audio
processing techniques to music content analysis. For example,
an automatic chorus detection method for compact disc (CD)recordings led to a new music listening station, called SmartMusicKIOSK [7]. Customers using conventional listening stations in music stores often want to jump directly to the chorusof a popular song, and SmartMusicKIOSK provides a “jump tochorus” button that enables the listener to jump to the next detected chorus section while skipping the sections of no interest.
This is an active listening environment in which a listener cancontrol the listening experience by interactively changing the
playback position. Pachet and Delerue [13] proposed an active
listening environment in which listeners can interactively spatialize sound sources. Unlike with SmartMusicKIOSK, however, this is achieved by using MIDI sound modules without
audio-based automatic content analysis.
In this paper, we describe a new active listening environment based on automatic music content analysis of musical

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(1) display of music title and artist name (2) stop button(3) play/pause toggle button (4) music file browser(5) playback slider (6) master volume slider(7) default restoring button(8) dropdown lists for selecting another timbre(9) drum-specific volume sliders

Figure 1: Screen snapshot of music player with INTER:D.
instruments. Listeners often focus on the melody line (e.g.,a sung melody in popular music) in musical pieces. Even if
the listener wants to emphasize the melody line in polyphonicmixtures by cutting the volume of accompaniment instruments(e.g., drums), he cannot do so with conventional graphic equalizers. Even if the listener is not comfortable with the timbreof the drums in a CD recording, she cannot replace it witha preferred one. To provide this capability, we developed adrum sound equalizer, called INTER:D (Instrument Equalizer
for Drums). In this paper, we use the term “equalizing” to meancontrolling the volume and timbre of instruments.
To equalize the bass and snare drum sounds, we cannot use
the conventional graphic equalizers that are often incorporated
into software or hardware music players. These equalizers cut
or boost the power in various frequency bands by applying
bandpass filters to the audio signal. However, they cannot control the volume of only the bass or snare drum. Moreover, it
is impossible to change the timbre of a drum without changing the timbre of other instruments. For instance, if the listenertries to cut the volume of the bass drum, the graphic equalizer
will attenuate only all the low-frequency sounds: it attenuates
the spectral components derived not only from the bass drum
but also from other musical instruments (e.g., snare drum andbass guitar).
On the other hand, INTER:D can control the volume andtimbre of only the bass or snare drum. Figure 1 shows the in-2 Equalizing Functions
INTER (Instrument Equalizer) is based on a novel equalizing
concept — the frequency components of each instrument, noteach frequency band, are adjusted. By applying this concept todrum sounds, we developed a drum-sound equalizer called INTER:D (INTER for Drums), which has two functions: volume
control (cutting or boosting the volume) and timbre change forbass and snare drum sounds. A listener can easily control thesefunctions by using a familiar easy-to-use interface, which isshown in Figure 1. In this section, we explain the implementation of these functions and their improvements.

2.1 Volume Control
INTER:D provides a volume cutting/boosting function for thebass and snare drum sounds in a musical piece containing
sounds of various instruments. Let X denote the target drumsound to be equalized. The algorithm is as follows (Figure 2):
1. Spectrogram Estimation: The power spectrogram of
drum sound X used in the musical piece is estimated.
Let PX denote that spectrogram. PX (t, f ) (1 ≤ t ≤T [frames], 1 ≤ f ≤ F [bins]) represents the local frequency component in the time-frequency domain (T and
F are fixed values).
2. Onset Detection: INTER:D detects the onset times ofdrum sound X in the monaural polyphonic audio signal

Onset-Detection

…

1

2…i

…

extract
fmusical audio signal…

…
onsetsReplacePi±frequency bin

teractive console of a music player equipped with INTER:D.
This player includes the conventional playback interface (playback slider and stop/pause/play buttons) in the upper part of thewindow. The added equalizing interface (volume sliders anddropdown lists) is in the lower part of the window. Listeners
can boost the volume of each drum sound by moving the appropriate slider from left (muted) to right (maximum volume)and vice versa. They can change the original timbre of eachdrum to their favorite timbre by selecting another one from thedropdown list. In this way, listeners can interactively control
their listening experience while receiving auditory feedback inreal time.
INTER:D is based on automatic content analysis for detection of the onsets of drum sounds and estimation of their spectrograms. For this analysis, we use our template-based drumtranscription system called AdaMast [21], which uses the spectrograms of drum sounds as templates. AdaMast is composedof template-adaptation and template-matching methods. First,
an initial spectrogram called a seed template is prepared foreach drum sound and adapted to the actual drum-sound spectrograms in the target piece. Next, every onset time at which theadapted template is included in the spectrogram of the target
piece is detected by using a carefully designed distance measure that is robust for spectral overlapping in sound mixtures.
The rest of this paper is organized as follows. Section 2
describes the equalizing functions of INTER:D. Section 3 explains why is needed AdaMast, and Section 4 explains its implementation. Section 5 describes the subjective experiments.
Section 6 discusses our developed technique, and Section 7
summarizes the key points.

1.0

r

ND

Pi′

PX

=

t

frame

power
original
spectrogram

power spectrogramof drum X

equalized
spectrogram

Spectrogram Estimation

Figure 2: Overview of cutting/boosting volume of drums.
of the piece.
3. Starting from each onset time, spectrogram segment Pi(i = 1, · · · , ND ) which has the same length as power
spectrogram PX is extracted from the input audio signal,
where ND is the number of detected onsets.
4. Power spectrogram PX is added to each spectrogram
segment Pi , weighted by arbitrary power change ratior (cut: −1 ≤ r < 0, boost: 0 < r):Pi (t, f ) = Pi (t, f ) + r · PX (t, f ),(1)

where Pi is an equalized spectrogram segment with aphase the same as that of the original segment before
equalizing.
5. An equalized spectrogram of the input audio signal is
obtained by replacing Pi with its Pi .
6. An equalized audio signal is obtained by applying
overlap-add synthesis to the equalized power spectrogram of the input audio signal.
To analyze input audio signals sampled at 44.1 [kHz], weused short-time Fourier transform (STFT) with a Hanning window (4096 points) with a shifting interval of 441 points (i.e.,
one frame is equivalent to 10 [ms]). T and F were experimentally set to 10 [frames] and 2048 [bins].
As described above, the system must automatically estimate the spectrograms of the drum sounds and detect their onset times. To annotate them, we applied our drum transcription
system called AdaMast, which is based on low-level signal processing. The details are described in Section 3.

2.2 Timbre Change
INTER:D also provides a function for changing the timbre of
drum sound X to that of another drum sound (let Y be that
drum sound). The algorithm is as follows:
1. To obtain an equalized audio signal in which the soundsof drum X are muted, power change ratio r is set to−1.0, and the volume control operations described above
are performed.
2. the audio signal of drum Y (a solo tone) is added to theequalized audio signal at each detected onset time.

maximum power

power

power changer ⋅ PMr ⋅ PX (t , f )PX (t , f )⋅ r ⋅ PX (t , f )PMpower change ratio

frame tdiscontinuity

frequency bin f

2.3 Quality Improvement
Although the basic operations described in Sections 2.1 and 2.2
are effective, there is much room for improving the sound quality. We therefore propose two methods for generating higherquality equalized audio signals: adjustment of the powerchange ratio and restoration of the temporal continuity.
2.3.1 Adjustment of Power Change Ratio
The estimated power spectrogram PX actually includes small
local frequency components of other musical instrument
sounds (i.e., PX cannot be the same as the precise power spectrogram of an isolated tone) because it is estimated from a complex polyphonic spectrogram. Therefore, if we use only PX forthe equalizing process, frequency components not derived fromthe target drum sound are also unnecessarily adjusted.
To solve this problem, we dynamically adjust power change
ratio r to minimize unnecessary power adjustment. As shownin Figure 3, we decrease r when adjusting the small local frequency components in PX . We replace r in Equation (1) with:PX (t, f )· r,PMdiscontinuity

2 [frames]
10 [frames]

Figure 3: Adjusting power change ratio in each frame and eachfrequency bin according to power in that position.

R(t, f ) =smoothing

boost

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frame

Figure 4: Smoothing frequency components along time axis
before and after equalized spectrogram segment.
our AdaMast drum transcription system [21]. It uses templateadaptation and template-matching methods, using the power
spectrogram of each drum sound as a template. In this section, we discuss the advantages of AdaMast for implementing
INTER:D.

3.1 Requirements
The spectrogram estimation and onset detection, must be performed for both the bass and snare drums.
Spectrogram Estimation The power spectrogram of the individual tone of each drum sound must be estimated in
the polyphonic spectrogram of the musical piece. The
spectrogram of each drum sound is not registered withthe system in advance because the sound characteristics
depend on the piece.
Onset Detection The onset times of each drum must be detected in the polyphonic spectrogram of the musicalpiece. This is difficult because various musical instrument sounds often overlap with the drum sounds.

where PM is the maximum local power in PX .

3.2 Approach
2.3.2 Restoration of Temporal Continuity
When power spectrogram PX is added to or subtracted from an
original spectrogram segment, the temporal continuity is deteriorated before and after the equalized spectrogram segment, as
depicted in Figure 4. This temporal discontinuity may have anegative influence on human music perception.
To solve this problem, we perform spectral smoothing in
the neighbor frames of each discontinuous frame. For each frequency band, frequency components in the range of 4 [frames](i.e., 2 [frames] before and after a discontinuous frame) aresmoothed using Savitzky-Golay’s smoothing method [18].

3 Drum Transcription Technique
To implement INTER:D, we must estimate the power spectrograms of the individual bass and snare drums (spectrogram estimation) and detect the onset times of each drum sound (onset
detection). Both are difficult because each musical piece has
different power spectrograms (sound characteristics) for the
drum sounds and there are many onset times of other sounds ina real-world sound mixture. To solve these problems, we used

To meet these requirements, we use the AdaMast drum transcription system. The template-adaptation method of AdaMast
first yields the power spectrogram (estimated sound characteristics) of each drum used sound in the input audio signal. The
template-matching method of AdaMast then detects its onset
times from the same audio signal.
Template Adaptation obtains the power spectrograms of theindividual bass and snare drum sounds in the input audio
signal. Before using this method, we must prepare individual power spectrograms (we called them seed templates in a previous study [21]) for both drums — two
templates in total. Note that the seed templates are different from the actual power spectrograms of the drumsounds in the input audio signal. This method adapts
each seed template to its corresponding power spectrograms of drum sounds included in the input audio signal.
Template Matching detects all the onset times of the bass andsnare drum sounds in the polyphonic audio signal, evenif other instrument sounds overlap them. To enable this,we designed a distance measure that is robust for polyphonic mixtures. Using this distance measure, we compare the adapted power spectrogram obtained by template adaptation with the power spectrogram of the input
audio signal to identify the onset times.
The adaptation and matching are sequential and are independently performed for transcription of the bass and snare
sounds. The detailed algorithms are described in Section 4.

3.3 Related Work
From the viewpoint of methodology, drum transcription methods are roughly categorized into three types: feature-based classification, sound source separation, and template-based detection. They can also be categorized by focusing on the complexity of the input audio signals: individual tones, drum tracks, ormusical pieces such as popular songs.
Feature-based classification methods are based on acoustic feature models trained using a database. Herrera et al. [11]compared conventional classifiers in experiments on identifying individual drum sounds. To transcribe drum sounds indrums-only audio signals, the use of N-grams [14], probabilistic models [15], and HMM&SVM [6] have been proposed.
To identify drum sounds extracted from polyphonic audio signals, Sandvolt et al. [17] proposed a feature-model adaptation
method that is robust to the distortion of features since feature
distortion caused by other sounds is a major problem.
Sound source separation methods, which are commonly
used, originated from spectrogram decomposition formulation
in independent subspace analysis (ISA) [1]. To transcribe drumsounds in audio signals of drum tracks, various assumptions are
made in decomposing a single music spectrogram into multiple spectrograms of drum tracks; ISA [3, 19] assumes the statistical independence of sources, non-negative matrix factorization (NMF) [16] assumes their non-negativity, and sparse
coding [20] assumes their non-negativity and sparseness. Further developments were made by FitzGerald et al. [4, 5]. They
proposed prior subspace analysis (PSA) [5], which assumes
the prior frequency characteristics of drum sounds, and applied it to transcribe drum sounds in the presence of harmonicsounds [4]. For the same purpose, Dittmar and Uhle [2] adopted
non-negative ICA that considers the non-negativity of sources.
To attain good separation results in any case, it is necessary toestimate the number of sources, but it is difficult to precisely
estimate it in general.
Template-based detection methods are based on a typical
pattern recognition approach — the distance between a template and an input pattern is calculated. Goto and Muraoka [9]proposed a template-matching method that uses spectrogram
templates, and transcribes drum sounds in drum-track audio
signals consisting of drum sounds only. Gouyon et al. [10]proposed a method that classifies mixed sounds extracted frompolyphonic audio signals into two categories (bass and snare
drums). To detect these drum sounds to be classified, they developed a template-adaptation method that uses waveform templates. It can deal with drum-sound variations found in musicalpieces. Zils et al. [22] extended Gouyon’s method to extraction

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(1) analyze/cancel toggle button(2) spin box for indicating analysis quality(3) checkbox for enabling/disabling logging of temporary spectrograms(4) checkbox for enabling/disabling dual processing(5) progress indicator of content analysis (spectrogram estimation and onset detection)

Figure 5: Console panel of AdaMast controller.
of bass and snare drum sounds from CD recordings. In general,
it is difficult to deal with the difference between a template andan actual pattern used in a musical piece. To deal with thisdifference in the time-frequency domain and achieve more robust performance, AdaMast [21] was developed by integrating
Goto’s matching method and Zils’ adaptation method.

3.4 Advantages of AdaMast
AdaMast is fast enough to achieve a stress-free music appreciation environment. This was an important factor in our decision
to use AdaMast to transcribe drum sounds. Figure 5 shows theconsole panel used to control AdaMast. Because content analysis (drum transcription) has a heavier load than the equalizing processing, the analysis parameters should be adjusted tomatch machine performance. If all options are applied, it willtake less than the length (playing time) of a musical piece —i.e., it is faster than the real-time performance — to complete
content analysis on a dual 3.0-GHz Xeon machine.

4 Implementation of AdaMast
As described above, AdaMast [21] is a template-based drumsound transcription system that consists of successive templateadaptation and template-matching. The former yields power
spectrogram PX , and the latter detects the onset times of thecorresponding drum sounds. These results are used by theequalizing functions of INTER:D.

4.1 Algorithms
The template-adaptation and template-matching methods canbe executed in parallel for the bass and snare drums.
4.1.1 Template Adaptation
This method obtains a spectrogram template that is adapted toits corresponding drum-sound spectrogram in the polyphonicmusical audio signal

FD ( f )pass ratio

Onset-Candidate Detection
spectrogram segments

P19

frequency

P1
…

P30

P31

P47

…

…

Template Updating

median

P62
…

P85
…

PN
…

power

FBD ( f )

0

frame

FSD ( f )

1.0

75

300
150
frequency binf

Segment Selection

frequency frame
seed template

Figure 7: Low-pass filter functions FBD and FSD represent typical frequency characteristics of bass and snare drums.

T1 T2 … TA…

iterative adaptation

adapted template

frequency bin

power

TS
T0

medianPi : spectrogram segment extracted from the i-th onset-candidate time
Tg : template after the g-th adaptive iteration

Figure 6: Overview of template adaptation method.

frame

audio signal. The adapted template is used as power spectrogram PX for the equalizing processing. Before starting theadaptation, it is necessary to prepare an arbitrary seed templatefor each bass and snare drum sound.
Our method is based on an iterative adaptation algorithm.
An overview is shown in Figure 6. First, onset-candidate detection stage roughly detects onset candidates in the input audio
signal of a musical piece. Starting from each onset candidate,a spectrogram segment with a fixed time length is extracted
from the power spectrogram of the input audio signal. Then,
using the seed template and all the spectrogram segments, theiterative algorithm successively applies segment selection andtemplate updating to obtain an adapted template.
Let Ti (i = 1 · · · ND ) denote a frame detected as an onset
candidate and Pi denote a spectrogram segment extracted from
Ti (ND is the number of detected onset candidates). These
selection and updating work as follows:
1. Segment selection calculates the reliability Ri that spectrogram segment Pi includes the drum sound spectrogram. The reliability is defined as the reciprocal of the
Euclidean spectral distance:
Ri = 
10 2048 
t=1

f =1

1

2 ,
T́g (t, f ) − Ṕi (t, f )(3)where Tg is the template after the g-th adaptive iteration.
In practice, we used a modified version of this measure.
T́g and Ṕi are low-pass filtered spectrograms:

selected spectrogram segments

updatedtemplate

Figure 8: Template updating by collecting median power at
each frame and each frequency bin for selected spectrogram
segments.
high reliabilities are then selected; this selection is basedon a fixed ratio to the total number of segments.
2. Template updating then reconstructs an updated templateby estimating the power that is defined, at each timeand each frequency bin, as the median power among theselected spectrogram segments (Figure 8). The median
operation can suppress harmonic components in the updated template. The template is thus adapted to the current piece and used for the next adaptive iteration. The
updated template, T́g+1 , is weighted by filter function
FD and is obtained byT́g+1 (t, f ) = median Ṕ (i) (t, f ),1≤i≤NS(6)

where P (i) (i = 1, · · · , NS ) are the spectrogram segments selected by segment selection. NS is the number of selected spectrogram segments, which is set to
0.1 × ND in this paper.
4.1.2 Template MatchingThis method detects all the onset times of the drum sounds
in the polyphonic audio signal, even if other musical instrument sounds overlap the drum sounds. To find the actual onsetT́g (t, f ) = FD (f ) Tg (t, f ),(4) times, this method determines whether the drum sound actuṔi (t, f ) = FD (f ) Pi (t, f ),(5) ally occurs at each onset candidate time, as shown in Figure 9.
The matching distance is calculated using Goto’s distance meawhere FD (f ) (D = BD, SD) is a low-pass filter function, sure [9]. Since this method focuses on whether the adapted
as shown in Figure 7. We assume that it represents the template is included in a spectrogram segment, it can calculate
typical frequency characteristics of bass drum sounds (BD) an appropriate distance even if the drum sound is overlappedand snare drum sounds (SD). Spectrogram segments with by other musical instrument sounds.

spectrogram segments

P19

frequency

P1
…

P30

P31

…

P47
…

P62
…

P85
…

To determine whether the target drum sound occurred ata time corresponding to spectrum segment Pi , distanceΓi is compared with threshold ΘΓ . If Γi < ΘΓ , we conclude that the target drum sound occurred. The ΘΓ isautomatically determined using Otsu’s thresholding algorithm [12].

PN
…

frame

power
Power Adjustment

TAw

FD ( f )×

Distance Calculation

4.2 Advantages of Using Two Distance Measures＝alternative determination

weightfilterfunctionfunction
Weight-Function Preparation

adaptedtemplate

Does each spectrogram segment
include the adapted template ?
answer : yes or no

Figure 9: Overview of template-matching method.

1. Weight-function preparation generates a function that
represents the spectral saliency of each frequency component in the adapted template. This function is used
for selecting characteristic frequency bins. The weightfunction w is defined asw(t, f ) = FD (f ) TA (t, f ),(7)

where TA is the adapted template, which is equivalent topower spectrogram PX used for the equalizing processing, and FD is the filter function.
2. Power adjustment calculates the power difference between the template and each spectrogram segment by focusing on the characteristic frequency bins. If the power
difference is larger than a threshold, it judges that thedrum sound spectrogram does not appear in that segment
and does not execute the subsequent processing. Otherwise, the power of that segment is adjusted to compensate for the power difference. Let Pi be a power-adjusted
spectrogram segment.
3. Distance calculation calculates the spectral distance between adapted template TA and each Pi . If Pi (t, f ) islarger than TA (t, f ), Goto’s distance measure regardsPi (t, f ) as a mixture of frequency components not onlyof the drum sounds but also of other musical instrument
sounds. In other words, if we determine that Pi (t, f ) includes TA (t, f ), then the local distance at frame t andfrequency bin f is minimized. Therefore, the local distance is defined as
 0 if (P  (t, f ) − T (t, f ) ≥ Ψ) ,Aiγi (t, f ) =(8)
1 otherwise,
where Ψ is a negative constant, which is set to −12.5[dB] in this paper.
Total distance Γi is calculated by integrating local distance γi in the time-frequency domain, weighted by w:
Γi =10 2048
t=1 f =1w(t, f ) γi (t, f ).

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We use two different distance measures between the template
adaptation and matching methods. In the adaptation method,
it is desirable to detect only semi-pure drum sounds that havelittle overlap with other sounds. Those drum sounds tend toresult in a good adapted template that includes few frequency
components of other sounds. Because it is not necessary todetect all the onset times of the target drum sounds, the distancemeasure does not need to consider spectral overlapping of othersounds. In the matching method, on the other hand, we used
Goto’s distance measure because it is necessary to exhaustively
detect all the onset times even if the target drum sounds areoverlapped by other sounds.

5 Evaluation
We performed subjective experiments using five songs takenfrom a popular music database, “RWC-MDB-P-2001” developed by Goto el al. [8]. These songs contain sounds of vocals
and various instruments, as songs in commercial CDs typicallydo. All original data were sampled at 44.1 kHz with 16 bits,
stereo. We converted them to monaural signals.

5.1 AdaMast
We first evaluated the onset detection accuracy of AdaMast.
The results are shown in Table 1. The power spectrograms andonset times of the bass and snare drums were obtained using
the template-adaptation and template-matching methods. The
power spectrograms were accurately estimated.

5.2 Volume Control
We tested INTER:D with five subjects to evaluate the qualityof volume control for two cases: a 10-[dB] cut and a 5-[dB]boost. Each subject listened to audio signals in which the volume of the bass or snare drum sounds was changed by using
INTER:D and audio signals in which the low-frequency sounds
were equalized by using a traditional graphic equalizer. They
compared these signals in terms of the impression of the negative effects on sounds other than the target drum sounds (bass or
snare drum sounds). The comparison results were numericallyscored: a value of 10 (reference in comparison) corresponds tothe impression of negative effects caused by the graphic equalizer; a value of 0 corresponds to the impression of almost no
negative effects.
The five subjects each gave four scores corresponding tofour cases (only the bass drum was attenuated, only the snare

MIR system based on stored annotation files

Table 1: Onset detection accuracy.
piece
No. 7
No. 21
No. 35
No. 47
No. 52bass
98.0 %
77.3 %
76.1 %
94.7 %
99.3 %

snare
85.7 %
84.7 %
74.5 %
74.8 %
90.8 %

average
91.9 %
81.0 %
75.3 %
84.8 %
95.1 %

content-based query
display titles of pieces
having similar contents
storage server

personal computer #4

upload annotation filesof unregistered musical pieces

download annotation filesof registered musical pieces

Table 2: Volume control evaluation results for INTER:D.
piece
No. 7
No. 21
No. 35
No. 47
No. 52
average

10 [dB] cut
bass
snare
3.6
5.0
3.2
4.0
3.6
5.0
3.8
5.0
3.0
4.0
3.4
4.6

5 [dB] boost
bass
snare
2.4
3.4
2.4
2.4
2.2
3.0
2.6
3.2
2.6
2.8
2.4
3.0

Note: This table shows the average score for negative effects
on other instrument sounds when the bass or snare drum sounds
were attenuated or amplified. The lower the score, the better the
impression of the sounds equalized by INTER:D. The scores
were averaged over the five subjects.

drum was attenuated, only the bass drum was amplified, andonly the snare drum was amplified) for each of the five songs1 .
Table 2 shows the results of this subjective evaluation.
These results of low scores indicate that the negative influence caused by INTER:D was much less than that caused by
the graphic equalizer. The scores for snare drum sounds werehigher (worse) than those for bass drum sounds: equalizing
snare drum sounds was more difficult than equalizing bass
drum sounds because frequency components of snare drumsounds are distributed in wide frequency bands. In addition,these results also indicate that the attenuation was more difficult (had worse scores) than the amplification. By comparing
Table 1 with Table 2, the detection accuracy was not related tothe evaluated scores at least in our experiments.

5.3 Timbre Change
We tested INTER:D with the same five subjects to evaluate
the quality of timbre change for two cases, timbre replacement
and timbre muting. Each subject listened to audio signals inwhich the bass and snare drum sounds used were replaced withother drum sounds with different timbres and to audio signals
in which the bass and snare drum sounds were muted (attenuated completely). The subject was then asked to complete a
subjective questionnaire about the impression made by thosesignals.
Most subjects answered that the timbre change (timbre replacement) was an interesting function because good-quality
audio signals were generated even though the impression madeby those signals with different drum sounds often greatly differed from that made by the original signals. We found that
1 The sound files are available at

http://winnie.kuis.kyoto-u.ac.jp/members/yoshii/EWIMT2005/.

personal computer #1
analysis and annotationof music contents

personal computer #2

personal computer #3

reuse of annotated contents
for local application (e.g., INTER:D)

Figure 10: Music information retrieval (MIR) system based on
automatic annotation and sharing of music contents.

accurate onset detection was more important for improving thesound quality when changing the timbre than when controllingthe volume. On the other hand, most subjects thought that theaudio signals in which the drum sounds were muted sounded
artificial. Muting drum sounds resulted in low-quality audio
signals because frequency components that did not belong to
the drum sounds were also largely attenuated. When otherdrum sounds were added in the timbre replacement, however,those additional sounds compensated for the undesired spectral
attenuation, resulting in good-quality audio signals.

6 Discussion
The importance of music content analysis for audio signals
has been increasing in the field of music information retrieval(MIR). MIR aims at retrieving musical pieces by executing
a query about not only text information such as artist names
and music titles but also musical contents such as rhythms andmelodies. Although the amount of digitally recorded music
available over the Internet is rapidly increasing, there are only
a few ways of using text information to efficiently find target
musical pieces in a huge music database. Music content analysis enables MIR systems to automatically understand the contents of musical pieces and to deal with them even if they do
not have metadata about the artists and titles. Although onset
time information for drum sounds is low-level music content,
it can be used as a basis for higher-level music content analysis
concerning the rhythm such as beat tracking and tempo estimation.
One reason we used AdaMast for drum transcription is thatit features fast content analysis, as described in Section 3.4. It
also is suitable for transmitting the analysis results over the Internet. Once the power spectrograms and onset times of thedrum sounds in a piece are analyzed, the drum-sound annotation files should be shared among people who have the same
piece in order to prevent computer resources from being wasted.
This framework of music content sharing can be considered an
extended version of the text-based CD Database (CDDB) service. Since INTER:D requires only a single spectrogram andthe onset times for each drum sound, the size of each annotation file is only about 300 [KB]. If we did not use AdaMast andinstead used a sound source separation method to implement
INTER:D, it would be necessary to transmit all the signals forthe drum tracks for equalizing the music to another computer.
However, the annotation file is too large (approximately over
100 [MB]) to be shared efficiently through the Internet. It isalso difficult to store many annotation files on a server. The fastand compact drum-sound annotation capability of AdaMast isthus important for practical use.
Figure 10 shows a diagram of an MIR system based on the
integration of low-level digital audio technology, semantic annotation, and content sharing. A user using a personal computer can download annotation files for musical pieces if they
are registered in a storage server or upload annotation files ifthey are not registered. A user can also retrieve musical pieces
that have contents similar to a query. Music content annotation
systems such as AdaMast can thus form the basis of Semantic
Web for music.

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Acknowledgements
tracks from polyphonic music using independent subThis research was partially supported by MEXT, Grant-in-Aid
space analysis,” ICA, 2003, pp. 843–848.
for Scientific Research, and by the COE program of MEXT.
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