Singing Voice Conversion Method Based on
Many-to-Many Eigenvoice Conversion and Training
Data Generation Using a Singing-to-Singing
Synthesis System
Hironori Doi∗ , Tomoki Toda∗ , Tomoyasu Nakano† , Masataka Goto† , and Satoshi Nakamura∗∗ Graduate School of Information Science, Nara Institute of Science and Technology (NAIST)

E-mail: {hironori-d, tomoki, s-nakamura}@is.naist.jp Tel: +81-743-72-5265† National Institute of Advanced Industrial Science and Technology (AIST)
E-mail: {t.nakano, m.goto}@aist.go.jp

Abstract—The voice quality (identity) of singing voices isusually fixed in each singer. To overcome this limitation andenable singers to freely change their voice quality using signalprocessing technologies, we propose a singing voice conversion
method based on many-to-many eigenvoice conversion (EVC)that can convert the voice quality of an arbitrary source singer
into that of another arbitrary target singer. Previous EVC-based
methods required parallel data consisting of song pairs of a single
reference singer and many prestored target singers for training
a voice conversion model, but it was difficult to record suchdata. Our proposed method therefore uses a singing-to-singing
synthesis system called VocaListener to generate parallel data byimitating singing voices of many prestored target singers withthe system’s singing voices. Experimental results show that our
method succeeded in enabling people to sing a song with the voice
quality of a different target singer even if only an extremely small
amount of the target singing voice is available.

I. I NTRODUCTION
Singing voices are different from sounds of musical instruments because singing voices can usually convey the linguistic
information of the lyrics as well as the pitch, dynamics,
and voice quality. Although a singer can expressively control
voice timbres to some degree, the singer usually has difficultychanging his or her voice quality (identity) of singing into thatof another singer. This is due to physical constraints in speech
production that limit expressive freedom when singing a song.
When using singing synthesis systems, people do not facesuch a limitation. Instead of using a physical body, people canartificially generate humanlike singing voices having different
voice qualities by changing the singing synthesis parameters.
Since 2007, many end users have started to use singing
synthesis systems, such as Vocaloid2 [1] and Sinsy [2], toproduce music, and the number of listeners enjoying synthesized singing voices has been increasing. Particularly inJapan, various compact discs that include synthesized vocal
tracks have often appeared in the popular music chart [3]. In
addition, several techniques that can change the timbre of auser’s or synthesized singing voice have been proposed [4],[5], [6], [7]. However, it is still difficult to generate singing
voices with arbitrary and desired voice qualities.

To make it possible for people to directly sing with adifferent specific voice quality, and thus overcome physicalconstraints, singing voice conversion has been proposed [8].
Statistical voice conversion techniques [9], [10], [11] are used
to convert the singing voice quality of a source singer intothat of a target singer. In this technique, a Gaussian mixture
model (GMM) of the joint probability density of an acoustic
feature between the source singer’s singing voice and the target
singer’s singing voice is trained in advance using a special dataset, called parallel data, that consists of song pairs of these
two singers. The trained model is capable of converting theacoustic features of the source singer’s singing voice into thoseof the target singer’s singing voice in any song while keeping
the linguistic information of the lyrics unchanged.
Towards realizing a more flexible singing-voice conversion
technique that enables singers to freely control the converted
singing voice quality and is capable of rapidly adapting theconversion model to arbitrary singers, we propose a singingvoice conversion method based on two techniques: many-tomany eigenvoice conversion (EVC) [12] and training data
generation using a singing-to-singing synthesis system [13].
Many-to-many EVC is a technique of conversion from thevoice of an arbitrary source singer into the voice of an arbitrarytarget singer. An eigenvoice GMM (EV-GMM) [14] is trainedin advance with multiple parallel data sets that consist of asingle predefined singer, called a reference singer in this paper,and many prestored target singers. The EV-GMM is capableof easily adapting the source/target voice quality to that of afew of their given voice samples in a text-independent (lyricsindependent) manner. By using this flexible voice conversion
technique, the proposed method enables any singer or end
user to freely control their singing voice quality. Furthermore,to easily develop multiple parallel data sets from nonparallel singing voice data sets of many singers, we propose atechnique for efficiently and effectively generating parallel
data sets using a singing-to-singing synthesis system called
VocaListener to artificially generate voices of the reference
singer. In this paper, we conduct objective and subjective

experimental evaluations to demonstrate the effectiveness ofthe proposed methods.
This paper is organized as follows. In Section II, we describe
the many-to-many EVC algorithm. In Section III, the singingto-singing synthesis system used in this paper is explained.
In Section IV, the proposed method of singing voice conversion is described. In Section V, experimental evaluations aredescribed. Finally, we summarize this paper in Section VI.
II. M ANY- TO - MANY EVC [12]In this section, we describe many-to-many EVC as atechnique for flexibly developing a conversion model for an
arbitrary speaker pair. This technique consists of a training
process, an adaptation process, and a conversion process.
A. Training process
As acoustic features of the reference speaker and the 𝑠𝑡ℎtarget speaker, we employ two 𝐷-dimensional joint features,
(𝑠)
(𝑠) ⊤
(𝑠) ⊤
⊤ ⊤
𝑿 𝑡 = [𝒙⊤, Δ𝒚 𝑡 ]⊤ , consisting𝑡 , Δ𝒙𝑡 ] and 𝒀 𝑡 = [𝒚 𝑡of 𝐷-dimensional static and dynamic features at frame 𝑡,
respectively, where ⊤ denotes the transposition of the vector.
The joint probability density of reference and target features
is modeled with the EV-GMM as follows:(𝑠)

𝑃 (𝑿 𝑡 , 𝒀 𝑡 ∣𝝀(𝐸𝑉 ) , 𝒘(𝑠) )()
𝑀∑(𝑠) ⊤ ⊤
(𝑋,𝑌 )(𝑠)=𝛼𝑚 𝒩 [𝑿 ⊤
,
𝒀
]
;
𝝁
,
Σ
𝑡
𝑡
𝑚
𝑚

(1)

𝑚=1

𝝁(𝑠)
𝑚 =

[

[ (𝑋𝑋) (𝑋𝑌 ) ]
]
(𝑋)
Σ𝑚
Σ𝑚
𝝁𝑚
(𝑋,𝑌 )
=
,
Σ
𝑚
𝑋)
𝑌) ,
Σ(𝑌
Σ(𝑌
𝑨𝑚 𝒘(𝑠) + 𝒃𝑚
𝑚
𝑚

(2)

where 𝒘(𝑠) = [𝑤(𝑠) (1), ⋅ ⋅ ⋅ , 𝑤(𝑠) (𝐽)]⊤ is the target-speakerdependent weight vector for controlling target voice quality.
𝝀(𝐸𝑉 ) is a canonical EV-GMM parameter set consisting(𝑋)of the weight 𝛼𝑚 , the mean vector 𝝁𝑚 , the covariance(𝑋,𝑌 ), the bias vector 𝒃𝑚 , and the basis vectors
matrix Σ𝑚
𝑨𝑚 = [𝒂𝑚 (1), ⋅ ⋅ ⋅ , 𝒂𝑚 (𝐽)] for the 𝑚𝑡ℎ mixture component,
where the number of basis vectors is 𝐽. Acoustic features of anarbitrary target speaker are modeled by setting only 𝒘(𝑠) to the
speaker’s specific values. The other parameters are the samefor all target speakers. The EV-GMM is trained by adaptive
training using multiple parallel data sets consisting of utterance
pairs of a reference and many prestored target speakers [15].
B. Adaptation and conversion process
In the adaptation process, the EV-GMM is adapted to anarbitrary source speaker and an arbitrary target speaker by
independently estimating the speaker-dependent weight vector
using a few speech samples. The weight vector for sourceˆ (𝑖) is estimated as
speaker 𝒘
𝑇 ∫
)
(
∏
(𝑖)
ˆ (𝑖) = argmax
𝑃 𝑿 𝑡 , 𝒀 𝑡 ∣𝝀(𝐸𝑉 ) , 𝒘 𝑑𝑿 𝑡 (3)
𝒘
𝒘 𝑡=1
(𝑖)

where 𝒀 𝑡 is the acoustic features of the given source
speaker’s voice at frame 𝑡. The weight vector for the targetˆ (𝑜) is estimated in the same manner. Then, the joint
speaker 𝒘

probability density of the acoustic features between the source
speaker’s voice and the target speaker’s voice is derived as
)
(
(𝑖)
(𝑜)
ˆ (𝑖) , 𝒘
ˆ (𝑜) , 𝝀(𝐸𝑉 )𝑃 𝒀 𝑡 , 𝒀 𝑡 ∣𝒘
∫
𝑀
)
(
∑
(𝑖)
(𝑖)
=
𝑃 (𝑚∣𝝀(𝐸𝑉 ) ) 𝑃 𝒀 𝑡 ∣𝑿 𝑡 , 𝑚, 𝒘𝑡 , 𝝀(𝐸𝑉 )
𝑚=1

) (
)
(
(𝑜)
(𝑜)
𝑃 𝒀 𝑡 ∣𝑿 𝑡 , 𝑚, 𝒘𝑡 , 𝝀(𝐸𝑉 ) 𝑃 𝑿 𝑡 ∣𝑚, 𝝀(𝐸𝑉 ) 𝑑𝑿 𝑡([
] [ (𝑖) ][ (𝑌 𝑌 ) (𝑌 𝑋𝑌 ) ])𝑀∑𝒚 (𝑖) 𝝁𝑚
Σ𝑚
Σ𝑚
=
𝛼𝑚 𝒩
;
,
(4)
(𝑌 𝑋𝑌 )𝑌)𝒚 (𝑜) 𝝁(𝑜)
Σ𝑚
Σ(𝑌
𝑚
𝑚
𝑚=1
where
𝑋𝑌 )
𝑋) (𝑋𝑋)
Σ(𝑌
= Σ(𝑌
Σ𝑚
𝑚
𝑚

−1

)
Σ(𝑋𝑌
.
𝑚

(5)

In the conversion process, the converted static feature sequence
vector is estimated using the adapted EV-GMM. Maximum
likelihood estimation considering dynamic features and a
global variance [11] is adopted.
III. S INGING - TO -S INGING SYNTHESISAs the singing synthesis system, a text-to-singing approach,which synthesizes a singing voice from note-level score information of the melody with its lyrics, such as Vocaloid2 [1],and a speech-to-singing approach, which synthesizes a singing
voice from speech samples of read lyrics by controlling
acoustic features [16], have been proposed. Moreover, singingto-singing synthesis, which automatically synthesizes a morenaturally sounding singing voice by estimating the parametersof the text-to-singing system from a target singing voice,has been proposed [13]. VocaListener [13] is the systemused for the estimation part of singing-to-singing synthesis.
VocaListener estimates parameters of pitch and dynamics forthe singing synthesis system so that the synthesized singing
voice becomes more similar to the target singing voice. If auser’s singing voice and the corresponding lyrics without any
score information are available, VocaListener can synchronize
them automatically to determine the musical note corresponding to each phoneme of the lyrics. Thus, the singing-tosinging synthesis system allows users to easily, speedily, andeffectively synthesize the singing voice.
IV. P ROPOSED SINGING VOICE CONVERSION METHOD
Figures 1 and 2 show the training process of a conventional
method using a standard GMM that converts the source
singer’s voice into the target singer’s voice and the training
and adaptation process of the proposed method using the EVGMM that converts an arbitrary source singer’s voice into anarbitrary target singer’s voice, respectively.
A. Singing voice conversion based on many-to-many EVC
As shown in Fig. 1, in the conventional method, the source
and target singers need to sing the same songs for several
minutes to generate parallel data for training the standard
GMM. On the other hand, as shown in the right part in Fig. 2,
in the proposed method, the GMM for the arbitrary sourceParallel data

Sing
Source singer

Training
GMM

Sing
Target singer

Fig. 1. Training process of conventional singing voice conversion.
Multiple parallel data sets
1st parallel data

2nd parallel data

Training
EV-GMMsth parallel data

Sth parallel data

Sing

Sing

Arbitrarysource singer

Arbitrarytarget singer
Estimation

sth parallel data
Sing

Estimation

Source singer’s
weight vector

Target singer’s
weight vector
Adaptation

sth prestored
target singer
Synthesis
Singing-to-singing
synthesis system(Reference singer)

Reference
singing voice

Parallel data generation for the sth prestored target singer

Adapted EV-GMM
Adaptation of the EV-GMM
to the arbitrary source and target singers

Fig. 2. Training and adaptation process of proposed singing voice conversion.

and target singers is easily built by adapting the EV-GMM toeach of these singers using weight vectors that are separately
estimated with only a small amount of their singing voices,e.g., for only several seconds. Moreover, since the adaptationof the EV-GMM is performed in a text-independent manner,
the singing voices of any song are usable as adaptation data;i.e., the source and target singers need not sing the same
song. Therefore, the proposed method effectively reduces theamount of effort required to prepare singing voices of thesource and target singers. Note that real-time singing voice
conversion is also achieved by using the low-delay conversion
algorithm [17].
B. Training data generation with singing-to-singing synthesis
Although the proposed singing voice conversion method
based on many-to-many EVC effectively and rapidly develops
the voice conversion model for the arbitrary source and target
singers, it needs to train the EV-GMM in advance using
multiple parallel data sets consisting of singing voice pairs of
the single reference singer and many prestored target singers.
The development of these parallel data sets is laborious work.
To address this issue, we artificially generate singing voices of
the reference singer by applying a singing-to-singing synthesis

system to singing voices of many prestored target singers.
In this approach, we need to prepare only singing voices ofmultiple prestored target singers who need not sing the same
song; these are available in existing databases, such as the
RWC Music Database [18]. As shown in the left part in Fig. 2,
for the singing voices of each prestored target singer, corresponding singing voices of the reference singer are artificiallygenerated by using the singing-to-singing synthesis system.
Then, a parallel data set is developed between singing voicesof each prestored target singer and the artificially generated
singing voices of the system’s singer as the reference singer.
Finally the EV-GMM is trained using many of the developed
multiple parallel data sets. Thus, this training data generation
approach can efficiently and effectively develop parallel data
sets without recording singing voices of the reference singer.
In the singing-to-singing synthesis, some acoustic parameters, such as phoneme duration and vibrato, of the synthesized
singing voice are automatically tuned so that they are similar
to those of the given target singing voice. Therefore, thesynthesized reference singing voice more closely corresponds
to the target singing voice than a singing voice sung by areal singer. Moreover, the voice quality of the singing voices

V. E XPERIMENTAL EVALUATIONS
To demonstrate the effectiveness of the proposed singing
voice conversion system, we conducted experimental evaluations.
A. Experimental conditions
We used solo singing voices of 30 Japanese songs in the
RWC Music Database [18] as the prestored target singing
voices. As the reference singing voices, we used singing
voices synthesized using the singing-to-singing synthesis system VocaListener with a singer database called Hatsune
Miku [19] based on Vocaloid2. As adaptation and test dataof source/target singing voices, we used the original female
solo singing voices of two Japanese songs in the RWC Music
Database (RWC-MDB-P-2001 No.35 and No.71), which were
not included in the above 30 songs, and also recorded singing
voices of another female singer for the same two songs.
The 1st through 24th mel-cepstral coefficients were used asa spectral parameter. STRAIGHT analysis [20] was employed
to extract these coefficients from singing voices. The shift
length was 5 ms and the sampling frequency was 16000 Hz.
The EV-GMM for spectral conversion was trained from
30 parallel data sets consisting of the synthesized reference
singing voices and the prestored target singing voices. The
number of basis vectors of the EV-GMM was set to 29. The
number of mixture components of the EV-GMM was set to128. As a conventional approach for reference (i.e., singing
voice conversion [8]), we also trained a standard GMM forspectral conversion using a parallel data set consisting of
the source and target singing voices. The number of mixture
components of the GMM was preliminarily optimized so thatspectral conversion accuracy was maximized in the test data.
As the training data for the conventional method and theadaptation data for the proposed method, 2, 4, 8, 16, 32, or
64% of the singing parts of a song (RWC-MDB-P2001 No.35)
sung by the source and target singers were used, and then, theremaining 36% of data were used for the test. The total lengthof this song was 193 s including 116 s of singing parts. We
evaluated two conditions of the song setting; 1) the same-song
condition, where the same song (RWC-MDB-P2001 No.35)
is used in both the training/adaptation process and the test
process, and 2) the different-song condition, where different
songs are used in the training/adaptation process (RWC-MDBP2001 No.35) and the test process (RWC-MDB-P2001 No.71).
B. Objective evaluation
We evaluated the spectral conversion accuracy of the conventional singing voice conversion and the proposed singing

8
Conventional method under same-song condition

Mel-cepstral distortion [dB]generated by the singing-to-singing synthesis system is moreconsistent, regardless of the song, genre of music, or thesinger’s physical condition than that of the singing voice of areal singer. These are favorable properties for training the EVGMM since, in the EV-GMM training algorithm, the reference
singing voices are assumed to basically have consistent voice
quality over all multiple parallel data sets.

Conventional method under differrnt-song condition

7.5

Proposed method under same-song condition
Proposed method under differrnt-song condition

7
6.5
6
5.5
5

2

4
8
16
32
Amount of training or adaptation data [%]64

Fig. 3. Mel-cepstral distortion as a function of amount of target singing
voice data (i.e., singing voice pairs in conventional method or singing voice
adaptation data in proposed method).

voice conversion using mel-cepstral distortion between theconverted and target mel-cepstra as an evaluation metric. Figure 3 shows mel-cepstral distortion as a function of the amountof singing voice adaptation data used in the proposed methodor the amount of parallel data of singing voice pairs used in theconventional method. Under the same-song condition, we cansee that the proposed method yields better spectral conversion
accuracy than the conventional method when using a small
amount of available data of the source/target singers (i.e., 2,
4, and 8%). Note that the proposed method does not require theuse of parallel data in adaptation but the conventional methodneeds to use them.
Under the different-song condition, the conventional method
shows much lower conversion accuracy than under the samesong condition. This is because the voice quality of the singing
voice significantly changes depending on the song even if
the same singer sings. On the other hand, it is observed thatthe proposed method reduces this degradation. Since the EVGMM is trained with many singers’ voices, it is more robust
against variations of the singing voice quality.
C. Subjective evaluation
We conducted an opinion test of voice quality and apreference test of singer individuality. The opinion score in theopinion test was set to a 5-point scale (i.e., 1 (very poor) to 5
(excellent)). In this test, listeners heard each converted singing
voice sample, then they judged the voice quality of eachsample using the opinion score. In the preference test, listeners
heard a target singing voice sample and two converted singing
voice samples, then they chose the converted singing voicesample that had more similar singer individuality to the target
singing voice sample. The preference test was independently
performed under the same- or different-song condition. In
these tests, 5 listeners evaluated 8 types of singing voices generated under various conditions consisting of all combinationsof the following: 2% or 64% of training/adaptation data, theMean opinion score (MOS)

4.5
4
3.5
3
2.5
2
1.5
1
2

64

2

Conventional

64

ProposedSame-song condition

2

64

2

Conventional

64

[%]

Proposed

Different-song condition

Fig. 4. Result of opinion test of voice quality.
100

Preference score [%]

90
80
70
60
50
40
30
20
10
0
2

64

Conventional264 [%]

Proposeda) Same-song condition

2

64

Conventional264 [%]

Proposedb) Different-song condition

Fig. 5. Result of preference test of singer individuality in a) same-song
condition and b) different-song condition.

conventional or proposed method, and the same- or differentsong condition.
Figure 4 shows the result of the opinion test of voice
quality. Under the same-song condition, the proposed method
using only 2% adaptation data yields better voice quality
than the conventional method using 2% parallel training data.
This is because the proposed method effectively uses theprestored target singers’ data to develop the conversion modelfor the new source and target singers. An increase in theadaptation data from 2% to 64% yields further voice quality
improvements in the proposed method but it does not reachthat in the conventional method using 64% parallel training
data. Note that the proposed method does not use the parallel
data set of the source and target singers, but the conventional
method does.
Under the different-song condition, a decrease in the parallel
training data from 64% to 2% in the conventional method
causes much greater voice quality degradation than that under
the same-song condition. This is because the acoustic charac-

teristics of singing voices are considerably different between
different songs. On the other hand, the proposed method yields
almost the same voice quality as that under the same-song
condition even when using only 2% adaptation data. As also
observed in the objective evaluation, the proposed method
is robust against variations of the singing voice quality. An
increase in the adaptation data from 2% to 64% yields further
voice quality improvements, and the voice quality is almost
equal to that in the conventional method using 64% parallel
training data. It is again worth noting that the proposed method
does not use the parallel data but the conventional method
does.
Figure 5 shows the results of the preference test of singer
individuality. The preference score was calculated as theratio of the number of samples selected as having better
singer individuality to the number of samples presented to
the listeners. We can see basically the same tendency as thatobserved in the results of the opinion test of voice quality.
The proposed method also yields a better result in conversion
accuracy for singer individuality when only 2% adaptation
data is available compared with the conventional method
using 2% parallel training data. This difference between the
proposed and conventional methods is much larger under thedifferent-song condition than under the same-song condition.
An increase in the adaptation data from 2% to 64% in the
proposed method also yields further improvements. Although
the result of the proposed method is still significantly worse
than the that of the conventional method when using 64%
parallel training data under the same-song condition, they arealmost equal under the different-song condition.
The above results suggest that 1) the proposed method
yields better voice quality and conversion accuracy for singer
individuality than the conventional method when a small
amount of singing voice data of the source and target singers
is available; 2) the proposed method is capable of effectively
using nonparallel data of the source and target singers to
rapidly develop the conversion model between these singers,
and 3) since the proposed method is robust against variationsof the singing voice quality often observed between different
songs, it works reasonably well even when using different
songs between the adaptation and conversion processes.
VI. C ONCLUSIONS
We have presented a singing voice conversion method basedon many-to-many EVC and training data generation using asinging-to-singing synthesis system. Our proposed method iscapable of converting the singing voice quality of an arbitrary
source singer into that of an arbitrary target singer by adapting
a small number of adaptive parameters of a conversion modelusing an extremely small amount of source and target singing
voice data. Moreover, our proposed method can alleviate theburden of having to record singing voices to develop parallel
data sets, by using a singing-to-singing synthesis system. The
experimental result demonstrated that the proposed method
enables the effective conversion of a singing voice between anarbitrary speaker pair even when using only several secondsof their singing voices as adaptation data.
VII. ACKNOWLEDGEMENTS
This work was supported in part by a MEXT Grant-in-Aid
for Young Scientists (A). The authors are grateful to professor
Hideki Kawahara of Wakayama University, Japan, for permission to use the STRAIGHT analysis-synthesis method.
R EFERENCES[1] H. Kenmochi and H. Ohshita, “VOCALOID – Commericial singing
synthesizer based on sample concatenation,” Proc. INTERSPEECH, pp.
4011–4012, Aug. 2007.
[2] K. Oura, A. Mase, T. Yamada, S. Muto, Y. Nankaku, and K. Tokuda,“Recent development of the HMM-based singing voice synthesis system- Sinsy,” SSW7, pp. 211–216, Sept. 2010.
[3] H. Kenmochi, “VOCALOID and Hatsune Miku phenomenon in Japan,”
Proc. InterSinging, pp. 1–4, Oct. 2010.
[4] H. Kawahara, R. Nisimura, T. Irino, M. Morise, T. Takahashi, and
H. Banno, “Temporally variable multi-aspect auditory morphing enabling extrapolation without objective and perceptual breakdown,” Proc.
ICASSP, pp. 3905–3908, Apr. 2009.
[5] F. Villavicencio and J. Bonada, “Applying voice conversion to concatenative singing-voice synthesis,” INTERSPEECH, pp. 2162–2165, Sept.
2010.
[6] T. Nakano and M. Goto, “Vocalistener2: A singing synthesis sytem able
to mimic a user’s singing in terms of voice timbre changes as well as
pitch and dynamics,” Proc. ICASSP, pp. 453–456, May 2012.
[7] Y. Yoshida, R. Nishimura, T. Irino, and H. Kawahara, “Vowel-based
voice conversion and its application to singing-voice manipulation,”
Proc. AES 35th International Conference: Audio for Games, no. 6, Feb.
2009.
[8] Y. Kawakami, H. Banno, and F. Itakura, “GMM voice conversion of
singing voice using vocal tract area function,” IEICE technical report.
Speech 110(297) (Japanese edition), pp. 71–76, Nov. 2010.
[9] Y. Stylianou, O. Cappe, and E. Moulines, “Continuous probabilistictransform for voice conversion,” IEEE Trans. SAP, vol. 6, no. 2, pp.
131–142, Mar. 1998.
[10] A. Kain and M. W. Macon, “Spectral voice conversion for text-to-speechsynthesis,” Proc. ICASSP, pp. 285–288, May 1998.
[11] T. Toda, A. W. Black, and K. Tokuda, “Voice conversion based on
maximum likelihood estimation of spectral parameter trajectory,” IEEE
Trans. ASLP, vol. 15, no. 8, pp. 2222–2235, Nov. 2007.
[12] Y. Ohtani, T. Toda, H. Saruwatari, and K. Shikano, “Many-to-many
eigenvoice conversion with reference voice,” INTERSPEECH, pp. 1623–
1626, Sept. 2009.
[13] T. Nakano and M. Goto, “VocaListener: A singing-to-singing synthesis
system based on iterative parameter estimation,” Proc. SMC 2009, pp.
343–348, May 2009.
[14] T. Toda, Y. Ohtani, and K. Shikano, “One-to-many and many-to-one
voice conversion based on eigenvoices,” Proc. ICASSP, pp. 1249–1252,
Apr. 2007.
[15] Y. Ohtani, T. Toda, H. Saruwatari, and K. Shikano, “Adaptive training
for voice conversion based on eigenvoices,” IEICE Trans. Inf. and Syst.,
vol. E93-D, no. 6, pp. 1589–1598, June 2010.
[16] T. Saitou, M. Goto, M. Unoki, and M. Akagi, “Speech-to-singing
synthesis: Converting speaking voices to singing voices by controlling
acoustic features unique to singing voice,” Proc. WASPAA, pp. 215–218,
Oct. 2007.
[17] T. Muramatsu, Y. Ohtani, T. Toda, H. Saruwatari, and K. Shikano, “Lowdelay voice conversion based on maximum likelihood estimation ofspectral parameter trajectory,” Proc. INTERSPEECH, pp. 1076–1079,
Sept. 2008.
[18] M. Goto, T. Nishimura, H. Hashiguchi, and R. Oka, “RWC Music Database: Music genre database and musical instrument sounddatabase,” Proc. ISMIR, pp. 229–230, Oct. 2003.
[19] Crypton Future Media, “What is the ”HATSUNE MIKU movement”?”
2012. [Online]. Available: http://www.crypton.co.jp/miku eng[20] H. Kawahara, I. Masuda-Katsuse, and A. Cheveigne, “Restructuring
speech representations using a pitch-adaptive time-frequency smoothing
and an instantaneous-frequency-based 𝑓0 extraction: Possible role of arepetitive structure in sounds,” Speech Communication, vol. 27, no. 3-4,
pp. 187–207, Apr. 1999.