Acoustic and Perceptual Effects of Vocal training in Amateur Male Singing
Takeshi SAITOU and Masataka GOTO
National Institute of Advanced Industrial Science and Technology (AIST){saitou-t,m.goto}[at]aist.go.jp

Abstractby summarizing the contributions of this research.

This paper reports our investigation of the acoustic effects of vocal training for amateur singers and of the contribution of thoseeffects to perceived vocal quality. Recording singing voices
before and after vocal training and then analyzing changes inacoustic parameters with a focus on features unique to singing
voices, we found that two different F0 fluctuations (vibrato andovershoot) and singing formant were improved by the training. The results of psychoacoustic experiments showed thatperceived voice quality was influenced more by the changes of
F0 characteristics than by the changes of spectral characteristics
and that acoustic features unique to singing voices contribute toperceived voice quality in the following order: vibrato, singing
formant, overshoot, and preparation.
Index Terms: singing voice, vocal training, psychoacoustic experiment

2. Singing voice data
We recorded amateur singers singing two Japanese popularsongs before and after vocal training. The singers were threemales who had never taken vocal training lessons, and the vocal
training teacher was a professional tenor with a 20-year teaching career. Each singer received a total of nine hours of vocal
training over three days. We also recorded the teacher singing
the same songs. The singing voices were recorded in soundproof room at a sampling rate of 48 kHz with 24-bit resolution by using a microphone (SHURE SM87A) and a solid-state
recorder (Marantz PMD671). The data was downsampled to 24
kHz and converted to 16-bit resolution. In this paper the singing
voices that were recorded before and after vocal training are respectively referred to as untrained and trained voices.
To evaluate qualities of untrained and trained voices subjectively, we conducted brief psychoacoustic experiments in which
the subjects were fifteen adults (nine male and six female) who
had at least two years of vocal training. None of the subjects
had hearing impairments. In the experiments the subjects listened to pairs of untrained and trained voices and judged whichof the pair was better. The voices were presented at a comfortable loudness level through binaural earphones (Sennheizer
HDA200). For both songs, all subjects judged the trained voices
to be better than the untrained voices.

1. Introduction
The expression of singing voices varies greatly according tosinger and singing style, and many amateur singers therefore
take vocal training lessons in an effort to acquire a professionallevel singing voice. Clarifying the acoustical and perceptual effects of vocal training will help us understand the mechanismsof singing voice production and perception, will help amateur
singers acquire a professional-level singing voice, and will contribute to the development of high-quality singing voice synthesis systems. In this study we therefore investigate the ways inwhich the acoustic features and perceived quality of the singing
voices of amateur singers were affected by vocal training.
Many studies have investigated the acoustic featuresunique to singing voices and some of them focused on relations between those features and the perceived voice quality.
Bartholomew dealt with the acoustic features of a good singing
voice [1], whereas Sundberg dealt with those affecting vocal
ugliness [2]. Saitou, on the other hand, investigated acoustic
features affecting perception of singing-ness [3]. Each of theseinvestigators found that the perception of voice quality depends
on acoustic features in fundamental frequency (F0) contour andspectrum, but the relations between those features and vocal
training are not clear.
This paper therefore reports our investigation of the acoustic and perceptual effects of vocal training in amateur singers
through acoustic analysis and two psychoacoustic experiments.
Section 2 introduces singing voice data recorded before and after vocal training. Section 3 focuses on four different acoustic
features unique to singing voices and shows the acoustic effects
of vocal training. Section 4 shows experimental results demonstrating the effects of F0 and spectrum on perceived vocal quality, and Section 5 presents experimental results showing that thefour different acoustic features in F0 and spectrum contribute to
the perception of voice quality. Section 6 concludes the paper

Copyright © 2009 ISCA

3. Acoustic effects of vocal training
To investigate acoustic effects of vocal training, we analyzed
features in F0 contour and spectrum by using STRAIGHT [4]and then compared these features between untrained and trained
voices.
3.1. F0 analysis
In the F0 contours of singing voices, there are several fluctuations unique to singing voices, and vibrato is the one usuallyrelated to professional voice quality [1, 2, 3]. Perceived quality of singing voices has also been reported to be influenced byother fluctuations [5]. We therefore analyzed three different F0
fluctuations, and compared each fluctuation between untrained
and trained voices.
vibrato: a quasi-periodic frequency modulation (4–7 Hz) [6].
overshoot: a deflection exceeding the target note after a notechange [5, 7].
preparation: a deflection in the direction opposite to a notechange observed just before the note change [5].
Figure 1 shows examples of the F0 contours of an untrained
voice and a trained voice, and also shows examples of the three832

6 - 10 September, Brighton UKTable 1: Characteristics of F0 fluctuations in untrained and trained voices of amateur singers and a professional singer (the vocal
training teacher).

singerA
B
C
pro

Characteristics of F0 fluctuations (untrained → trained)
VibratoOvershootPreparation
rate (variance) [Hz] extent (variance) [cent] extent [%] duration [ms] extent [%] duration [ms]5.2(2.6) → 5.3(1.4)
4.9(3.1) → 4.8(3.3)
5.3(3.3) → 5.2(2.6)

41(10.2) → 52(6.6)
43(23.4) → 49(16.1)
48(20.8) → 57(14.3)

18 → 1416 → 17
17 → 13223 → 196
204 → 222
198 → 163

10.7 → 10.3
11.6 → 10.9
9.3 → 8.9

126 → 130136 → 128
113 → 115

6.3(1.3)65(9.5)

11

126

9.8

119

6

relative amplitude [dB]

80

5.8
5.6
5.4

logF0

5.2
5
0
6
5.8

untrained
1000

3000

5000

7000

40
20
0

before vocal training
after vocal training

90000

preparation

vibrato

1000

2000

3000

4000

5000

6000

7000

8000

frequency [Hz]overshoot

5.6

Figure 2: Spectral envelopes of the vowel /a/ in the untrained
and trained voices of singer A.

5.4
5.2

trained
1000

3000

5000

7000

song 120

song 29000

increase [dB]

5
0

60

time [ms]Figure 1: F0 contours of singing voices of singer C.

15
10
5
0

A

B

C

singerF0 fluctuations. Characteristics of the F0 fluctuation in the amateur and professional singing voices are listed in Table 1.

Figure 3: Increased amplitude (mean ±SE) of spectral peaks
near 3 kHz in two songs sung by the amateur singers.

Vibrato was analyzed by measuring its rate (modulation frequency) and extent (amplitude). In each singer, the vibrato extent of the trained voice was larger than that of the untrained
voice and approximated the vibrato extent of the professional
singer’s voice. And because the variance of vibrato extent ineach of the trained voices was smaller than that in each of thecorresponding untrained voice, the vibrato in the trained voices
was steadier than that in the untrained voices. The vibrato rateand its variance, however, were not improved by vocal training.
These results indicate that vibrato rate is harder to control thanvibrato extent.

3.2. Spetcrum analysis
Sundberg showed that the spectral envelope of a male operatic
singing had a remarkable peak called the “singing formant” near
3 kHz and that the peak strongly affected the perceived quality of the singing voice [9]. Because a singing formant has
also been reported to be important in some traditional Japanesesinging [10], we analyzed the effects of vocal training on spectral peaks near 3 kHz.
Examples of spectral envelopes of the vowel /a/ in thesinging voices of singer A are shown in Figure 2, where one seesthat a spectral peak near 2.6 kHz is higher in the trained voice.
Increases in the amplitude of the spectral peak near 3 kHz after
vocal training are shown in Fig. 3. This figure shows that the
spectral peaks, in both songs of all singers, increased by vocal
training and the increases in the peaks was 13 dB on the average.
On the other hand, the spectral peaks in professional voices was
18 dB higher on average than that in untrained voices. These results suggest the possibility that a singing formant is generated
by vocal training.

Overshoot was analyzed by measuring its extent and duration. The extent indicates an F0 variance exceeding the target
note and the duration means a stabilization time. As shownin Table 1, for two of the amateur singers (A and C) the vocal training reduced the extent and duration of the overshoot toamounts approximating those in the voice of the professional
singer. Since Krom [8] had reported that controlling overshootwas one of the expression ways in professional singing, theanalysis results indicate that the vocal technique of singer A
and C was improved by training.
Preparation was also analyzed by measuring its extent andduration. Although vocal training changed both of these characteristics, the changes were smaller than the training-inducedchanges in the extent and duration of overshoot.

4. Psychoacoustic experiment 1
Psychoacoustic experiment 1 was conducted to investigate how
perceived voice quality is affected by the F0 contour and spec-833

average evaluation value

4

average evaluation value

4

than by the changes of spectral characteristics.
singer A

3

singer B
singer C

2

5. Psychoacoustic experiment 2

1
0

0

20

40

60

morphing rate [%]80

Psychoacoustic experiment 2 was conducted to examine thecontributions of three different F0 fluctuations (vibrato, overshoot, and preparation) and singing formant to the perceptionof voice quality.

100

singer A

3

singer B

5.1. Stimuli

singer C

2

Stimuli were made by using our recently developed system thatcan synthesize a singing voice when given the musical score ofa song and a speaking voice reading the lyrics of that song [12].
As shown in Fig. 5, the system is based on STRAIGHT andcomprises three models controlling three acoustic parameters:
the F0, phoneme duration, and spectrum. The three F0 fluctuations and singing formant can be controlled by the F0 andspectral control models. In this experiment we put the untrained
singing voices of the three amateur singers into the system andmade six different singing voices by replacing characteristics ofthe four acoustic features with professional characteristics.

1
0

0

20

40

60

morphing rate [%]

80

100

Figure 4: Result of the psychoacoustic experiment 1: the evaluation value of voice quality for F0 (upper fig.) and spectral(lower fig.) morphing.

trum changes resulting from vocal training.

UNTRAINED: singing voice for which all acoustic features
are controlled with untrained characteristics.

4.1. Stimuli
Two kinds of stimulus sets were made by using STRAIGHTmorphing [11]. The first set consisted of singing voices madeby morphing F0 contour from the untrained voice to the trained
voice. Stimuli of the second set were made by morphing spectral envelopes. In making each stimulus set, morphing rate waschanged from 0 to 100% in 10% steps. The morphing rate ”0%”corresponds to the untrained voice and ”100%” corresponds tothe trained voice. There were 66 stimuli in each set (11 morphing rates × 3 singers × 2 songs).

PRO-VB: singing voice for which only the UNTRAINED vibrato is controlled with professional characteristics.
PRO-OS: singing voice for which only the UNTRAINED
overshoot is controlled with professional characteristics.
PRO-PR: singing voice for which only the UNTRAINED
preparation is controlled with professional characteristics.
PRO-SF: singing voice for which only the UNTRAINED
singing formant is controlled with professional characteristics.

4.2. Methods
The experiment was conducted in accordance with the paired
comparison methodology. The subjects were sixteen adults(eleven male and five female) with normal hearing ability. All
had taken vocal training lesson for over a year.
The subjects listened to pairs of stimuli presented in random order at intervals of 500 ms. The first stimulus of a pair
was an untrained voice, and the second was a morphed singing
voice of the stimulus sets. The stimuli were presented at a comfortable loudness level through binaural earphones (Sennheizer
HDA200). Since the subjects listened to each pair only once,
the number of trials was 132.
The subjects were asked to evaluate a vocal quality of thesecond stimulus relative to that of the first on a five-grade scale:
0 for worse, 1 for almost the same, 2 for slightly better, 3 forclearly better, and 4 for much better. To confirm the criterionof judgment, the subjects had a rehearsal session with ten pairs
prior to each actual experiment. The experiment was conductedin a sound-proof room.

PRO-ALL: singing voice for which all acoustic features arecontrolled with professional characteristics.
In synthesizing each stimulus, the F0 contour was generated first by adding the three fluctuations to a melody contour
which is an input of the F0 control model, and then the generated F0 contour was replaced with that of the input singing
voice. The untrained and professional characteristics for each
F0 fluctuation were set to the values shown in Table. 1. Spectral envelopes, on the other hand, were generated by emphasizing the spectral peak near 3 kHz by 18 dB during vowel portionof the input singing voice. By using the generated F0 contour
and modified spectral envelopes, all stimuli were synthesized.
5.2. Methods
The experiment was conducted in accordance with the Scheffe’s
paired-comparison method (Ura’s modified method) [13]. The
subjects were the same as the experiment 1.
The subjects listened to pairs of stimuli presented in random order at intervals of 500 ms. Each pair consists of two of
the stimuli, the total number of the pairs was 180 (=6 P2 × 3
singers × 2 songs). The subjects were asked to evaluate the vocal quality of stimuli on a seven-step scale ranging from “-3”(The first stimulus is very good singing in comparison with thesecond) to “+3” (The second stimulus is very good singing incomparison with the first). Experimental environments were thesame as in experiment 1.

4.3. Results
Figure 4 shows experimental results. The horizontal and vertical axes respectively show a morphing rate and an evaluation
value. As shown in each figure, the evaluation value of thestimulus was increased steadily when the morphing rate wasincreased. Moreover, in the case of over 60% morphing, theperceptual effect of F0 morphing is obviously larger than that ofspectral morphing. These results indicate that perceived voice
quality is affected more by the changes of F0 characteristics

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Musical notes(musical score)

Speaking voice
Synchronization
information

paring acoustic features unique to singing voices in recordings
made before and after vocal training, we found that characteristics of two kinds of F0 fluctuations (vibrato and overshoot) andsinging formant were changed by vocal training. Then resultsof psychoacoustic experiments showed that changes of F0 characteristics affected voice quality more than changes of spectral
characteristics did and that acoustic features affecting the perception of the quality of singing voices can be ranked in thefollowing descending order of importance: vibrato, singing formant, overshoot, and preparation. In future work we will investigate the relations between acoustic features and vocal training
in more detail and use the results of those investigations to develop a high-quality singing voice synthesis system.

STRAIGHT(analysis part)Speech-to-singing synthesis system

F0 contour
Aperiodicity
index (AP)Spectralenvelope

Duration control modelF0 control
model

Spectral control
model 1
Modified
spectral envelope

F0 contourof singing voice

Modified AP

STRAIGHT(synthesis part)

7. Acknowledgements
We thank Ken-Ichi Sakakibara for many useful comments andmuch good advice. This work was supported by CrestMuse,CREST, JST.

8. References

Spectral control
model 2

[1] W. T. Bartholomew, “A Physical Definition of ”Good VoiceQuality” in the Male Voice,” J. Acoust. Soc. Am., Vol.55, 838844, 1934.

Synthesized
singing voice[2] J. Sundberg, “The KTH synthesis of singing,” Advances in Cognitive Psychology. Special issue on Music Performance, 2(2-3),
131-143, 2006.

Figure 5: Block diagram of the speech-to-singing synthesis system [12].
).45)-0 39).22.
0-0
IN R(S(APOTR OOUN PR
PR

)

(
ED

-0.8

-0.6

-0.4

-0.2

0
0.1

F(

-S

O
PR

0

0.2

poor singing

O
PR

-V

0.4

B(

2
0.3

)

O
PR

-A

LL

.5
(0

[3] T. Saitou et al., “Analysis of acoustic features affecting “singingnes” and its application to singing voice synthesis from speaking
voice,” Proc. ICSLP2004, Vol. III, pp. 1929-1932, 2004.
[4] H. Kawahara, et al., “Restructuring speech representations using
a pitch adaptive time-frequency smoothing and an instantaneousfrequency based on F0 extraction: Possible role of a repetitive
structure in sounds,” Speech Commun., Vol. 27, pp. 187-207,
1999.

5)

0.6

[5] T. Saitou, et al., “Development of an F0 control model based on
F0 dynamic characteristics for singing-voice synthesis,” SpeechCommun., Vol. 46, pp. 405-417, 2005.

0.8

good singing[6] C. E. Seashore, “The Vibrato,” University of Iowa Studies in thePsychology of Music, Vol. I, 1932.

Figure 6: Results of the psychoacoustic experiment 2: subjectively evaluated degree of the vocal quality for 6 stimuli.

[7] H. Mori, et al., “F0 dynamics in singing: Evidence from the dataof a baritone singer,” IEICE Trans. Inf. & Syst., Vol. E87-D, No.
5, pp. 1086-1092, 2004.
[8] G. de Krom, et al., “Timing and accuracy of fundamental frequency changes in singing,” Proc. ICPhS 95, Vol. I, pp. 206-209,
1995.

5.3. Results
Figure 6 shows the experimental results. The numbers under
the horizontal axis indicates the degree of the vocal quality ofthe stimuli. The result of the F-test confirmed that there are
significant differences amongst all stimuli at the 5% critical rate.
This shows that the vocal quality of the stimuli can be increased by converting characteristics of acoustic features to professional ones. Moreover, the value of PRO-VB was better
than that of PRO-OS, PRO-PR, and PRO-SF, indicating thatthe perceptual effect of vibrato was greater than that of any of
the other three acoustic features. These results also show thefollowing ranking (in decreasing order of importance) of theacoustic features with regard to their effects on perceived vocal
quality: vibrato, singing formant, overshoot, and preparation.
However, overshoot and preparation contributed to perceived
vocal quality less than the other features.

[9] J. Sundberg, “The Science of Singing Voice,” Northern Illinois
University Press, 1987.
[10] I. Nakayama, “Comparative studies on vocal expression in
Japanese traditional and western classical-style singing, using acommon verse,” Proc. ICA2004, 1295-1296, 2004.
[11] H. Kawahara, and H. Matsui, “Auditory Morphing based on an
Elastic Perceptual Distance Metric in an Interference-free Timefrequency Representation,” Proc. 2003 IEEE ICASSP, I, pp. 256259 (2003).
[12] T. Saitou, et al., “Speech-To-Singing Synthesis: Converting
Speaking Voices to Singing Voices by Controlling Acoustic Features Unique to Singing Voices,” Proc. 2007 Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 215-218(2007).
[13] S. Ura, et al., “Sensory Evaluation Handbook (in Japanese),”JUSE Press Ltd., pp. 366-384, 1973.

6. Conclusion
This paper described our investigation of the acoustic and perceptual effects of vocal training in amateur male singing. Com-

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