2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)VOCAL TIMBRE ANALYSIS USING LATENT DIRICHLET ALLOCATION AND
CROSS-GENDER VOCAL TIMBRE SIMILARITY
Tomoyasu Nakano

Kazuyoshi Yoshii

Masataka Goto

National Institute of Advanced Industrial Science and Technology (AIST), Japan

1. INTRODUCTION
The vocal (singing voice) is an important element of music in various musical genres, especially in popular music. Indeed, the vocal
timbre and singing style can influence people’s decision on whichsongs to listen to. In fact, several music information retrieval (MIR)
systems based on vocal timbre similarity have been proposed [1–5].
When people listen to singing voices, they can feel that different vocal timbres and singing styles share some factors that characterize
their timbres and styles. It is, however, not easy to define every factor even by singers themselves because such factors are latent. We
call these shared factors “latent topics”. The aim of this study is
to explore the latent topics of singing voices by deriving them frommany singing voices sung by different singers. The latent topics areuseful for MIR as well as singing analysis.
There are many reports of research on automatic estimation of
singing characteristics from audio signals: characteristics such as
voice category (e.g., soprano or alto) [6, 7], gender [8–10], age [10],body size [10], race [10], vocal register [11], singing modeling (F0 ,power, and spectral envelope) [12–19], breath sound [20,21], singing
skill [6,7,22–25], enthusiasm [26], F0 dynamics and musical genres[27], and the language of the lyrics [28–31] have been previously
proposed. The above previous works, however, have not revealed
latent topics that are shared by different singing voices.
To explore shared latent topics of voice timbres or singing styles,
we propose a vocal timbre analysis method based on a topic modeling method called latent Dirichlet allocation (LDA) [32]. In LDA,
each singing voice is represented as a weighted mixture of multiple
topics shared by all the singing voices in our song database. The

978-1-4799-2893-4/14/$31.00 ©2014 IEEE

5239

weightA
topic

weighttime

Dgeneration

cross-gender
vocal timbre similarity(KL2)

B
topic visualization
by singer cloudweightpitch-shifted
singing voices

vocal timbre similarity
(KL2)

C
topic

feature extraction(vocal timbre)

time

time
weight...

Index Terms— vocal timbre, cross-gender similarity, music information retrieval, latent Dirichlet allocation, word cloud

topic modelingsinging voices...

ABSTRACTThis paper presents a vocal timbre analysis method based on topic
modeling using latent Dirichlet allocation (LDA). Although many
works have focused on analyzing characteristics of singing voices,none have dealt with “latent” characteristics (topics) of vocal timbre,
which are shared by multiple singing voices. In the work describedin this paper, we first automatically extracted vocal timbre featuresfrom polyphonic musical audio signals including vocal sounds. The
extracted features were used as observed data, and mixing weights
of multiple topics were estimated by LDA. Finally, the semantics ofeach topic were visualized by using a word-cloud-based approach.
Experimental results for a singer identification task using 36 songs
sung by 12 singers showed that our method achieved a mean reciprocal rank of 0.86. We also proposed a method for estimating
cross-gender vocal timbre similarity by generating pitch-shifted(frequency-warped) signals of every singing voice. Experimental results for a cross-gender singer retrieval task showed that our method
discovered interesting similar pitch-shifted singers.

time

topic

Fig. 1. Overview of topic modeling of singing voices: vocal timbre
similarity, cross-gender vocal timbre similarity, and topic visualization by singer cloud.

mixing weights of LDA can be used to compute singing voice similarity for MIR (Fig. 1, A ) and to visualize the semantics of each
topic by using a word-cloud approach (Fig. 1, B ).
Moreover, we also propose a method for estimating crossgender vocal timbre similarity (Fig. 1, C ). For this estimation,
pitch-shifted (frequency-warped) audio signals of all singing voices
are automatically generated (Fig. 1, D ). For instance, by shifting up
the pitch of a male singing voice, we are able to obtain a female-likesinging voice. By using such pitch-shifted singing voices as queries
for MIR based on the latent topics of singing voice timbres, we canfind interesting cross-gender pairs of similar singing voices.
The remainder of this paper is structured as follows. Section
2 describes the proposed vocal timbre analysis method and crossgender similarity estimation method. Section 3 describes two experiments we used to evaluate the methods. Section 4 concludes thepaper by summarizing the key outcomes and discusses future work.
2. METHOD
This section describes a method of singing analysis by latent Dirichlet allocation (LDA) [32], and a method for estimating cross-gender
vocal timbre similarity. We deal with vocal timbre features extracted
from polyphonic musical audio signals including vocal sounds.
The cross-gender similarity is computed after first generating pitchshifted (frequency-warped) signals of all the target songs.

α (0)

π

β(0)

φ

zd,n

xd,n

The latent variable of the observed singing voice X d is Z d ={z d,1 , ..., z d,Nd }. The number of topics is K, so zd,n indicates a Kdimensional 1-of-K vector. Hereafter, all latent variables of singing
voice D are indicated Z = {Z 1 , ..., Z D }.
Figure 2 shows a graphical representation of the LDA model
used in this paper. The full joint distribution is given byK

N

p(X, Z, π, φ) = p(X|Z, φ)p(Z|π)p(π)p(φ)

DFig. 2. Graphical representation of the latent Dirichlet allocation(LDA). First the finite sets of mixing weights π of the multiple topics and the unigram probabilities φ of the singing words are stochastically generated according to Dirichlet prior distributions. Then oneof K topics is stochastically selected as a latent variable z d,n according to a multinomial distribution defined by π. Finally the singing
word xd,n is stochastically generated according to a multinomial
distribution defined by φ.

where π indicates the mixing weights of the multiple topics (D ofthe K-dimensional vector) and φ indicates the unigram probabilityof each topic (K of the V -dimensional vector). The first two terms
are likelihood functions, the other two terms are prior distributions.
The likelihood functions themselves are defined as
Kxd,n,v
Nd V
D 

 
zd,n,kφk,v,(2)p(X|Z, φ) =d=1 n=1 v=1p(Z|π)
There are previous works related to latent analysis of music,
such as music retrieval based on LDA of lyrics and melodic features [33], chord estimation based on LDA [34, 35], combining document and music spaces by latent semantic analysis [36], music recommendation by social tag and latent semantic analysis [37], andmusic similarity based on the hierarchical Dirichlet process [38].
The self-organizing map (SOM) can be latent analysis, and SOMbased music clustering has been proposed [39]. Futhermore, there
exist many research papers on acoustic analysis based on topic modeling (see, for example [40–43]). There are, however, none that dealt
with singing features.
2.1. Feature extraction of vocal timbre
To extract vocal timbre features, we use modules of Songle [44],our Web service for active music listening. We first use Goto’s
PreFEst [45] to estimate the F0 of the melody, and then LPMCC(mel-cepstral coefficients of LPC spectrum) of vocal and ΔF0 are
estimated by using the F0 and are combined them as a feature vector
at each frame. Then reliable frames are selected as vocal by using
a vocal GMM and a non-vocal GMM (see [3]). Finally, all feature
vectors of the reliable frames are normalized by subtracting the mean
and dividing by the standard deviation.
2.2. Converting vocal timbre features to symbolic informationby using a k-means algorithm
LDA deals with symbolic information (e.g. text), not continuous feature values as described in subsection 2.1 This paper therefore propose that the vocal features are converted to symbolic time series byusing a k-means algorithm. We call these symbolic representationsof singing singing words.

The observed data we consider for LDA are D independent singing
voices X = {X 1 , ..., X D } already converted to symbolic time series as described in 2.2. A singing voice X d is Nd symbolic time series X d = {xd,1 , ..., xd,Nd } which are the reliable frames (see 2.1).
The size of the singing words vocabulary is equivalent to the numberof clusters of k-means algorithm (= V ), xd,n is a V -dimensional“1-of-K” vector (a vector with one element containing a 1 and allother elements containing a 0).

5240

=

Nd V
D 



k=1
z

d,n,k
πd,k
.

(3)

d=1 n=1 v=1

We then introduce conjugate priors as follows:
p(π)

=

D


Dir(π d |α(0) ) =

d=1

p(φ)

=

K


D


C(α(0) )d=1

Dir(φk |β (0) ) =

k=1

K


K


(0)−1

α
πd,k

, (4)

k=1

C(β (0) )

V


(0)−1

φβk,v

, (5)

v=1

k=1

where p(π) and p(φ) are products of Dirichlet distributions. α(0)and β (0) are hyperparameters; C(α(0) ) and C(β (0) ) are normalization factors calculated as follows:
C(η) =

Γ(η̂),Γ(η1 ) · · · Γ(η|η| )

η̂ =

|η|


ηi

(6)

i=1

2.4. Singer identification by computing vocal timbre similarity
Similarity between two songs is defined in this paper as the inverseof the symmetric Kullback-Leibler distance (KL2) between two distributions, as follows:
dKL2 (π A ||π B ) =KπA (k) log

πA (k)
πB (k)

πB (k) log

πB (k),πA (k)

k=1

+

K

k=1

(7)

Here the mixing weights of a singing A is π A and the mixing
weights of a singing B is π B , and these are normalized to meet theprobability criterion.
K

2.3. LDA model formulation

(1)

k=1

πA (k) = 1,KπB (k) = 1

(8)

k=1

2.5. Topic visualization by using a word-cloud-based approach
The mixing weight of each song π is a D,K-dimensional vector(D × K matrix) which means that “π shows the predominant topics
of each song d.” The mixing weights can be useful for singer identification and cross-gender similarity estimation as described above inSinger nameASIAN KUNG-FU GENERATIONBUMP OF CHICKEN
Fukuyama Masaharu
GLAY
Hikawa Kiyoshi
Hirai Ken
aiko
JUDY AND MARY
Hitoto Yo
Tokyo Jihen
Utada Hikaru
Yaida Hitomi

Gender
Male
Male
Male
Male
Male
Male
FemaleFemaleFemaleFemaleFemaleFemale# of songs
3
3
3
3
3
3
3
3
3
3
3
3

Top 3 similarity songs are filled in black（■）.

Singer ID

M1 M2 M3 M4 M5 M6 F1 F2 F3 F4 F5 F6

ID
M1
M2
M3
M4
M5
M6
F1
F2
F3
F4
F5
F6

M1 M2 M3 M4 M5 M6 F1 F2 F3 F4 F5 F6Table 1. Singers of the 36 songs used in the experimental evaluation.

similarhighSinger ID

M1 M2 M3 M4 M5 M6 F1 F2 F3 F4 F5 F6low

M1 M2 M3 M4 M5 M6 F1 F2 F3 F4 F5 F6Singer ID (Query)

Singer ID (Query)Fig. 3. A similarity matrix based on the mixing weights of topics.
rank
10

this Section. However, it is difficult to explain of semantic of each
topic from the mixing weights.
This subsection considers the topic weights π as a K,Ddimensional vector. This means that “π shows the predominantsongs for each topic k.” It is utilized to interpret the semantics ofeach topic by showing a word cloud, which is one of word visualization methods frequently used on the web. We call this word
cloud singer cloud. In the singer cloud, metadata of a singing (e.g.
a singer’s name or a song name) are visualized according to themixing weights. In this paper, predominant singers of each topic are
visualized with large size.
2.6. Cross-gender similarity by generating pitch-shifted signals
This paper describes a method for cross-gender similarity estimation. Pitch-shifted signals are generated by shifting them up/down
the frequency axis according to the results of short-term frequency
analysis. This shifting is equivalent to changing the shape of asinger’s vocal tract.
All of these pitch-shifted signals are generated by using SoX1 .
3. EXPERIMENTAL EVALUATION
The proposed methods were tested in two experiments, one evaluating the singer identification and the other evaluating the cross-gender
vocal timbre similarity estimation.
The songs used in these experiments were monaural 16-kHz digital recordings. The singers are listed in Table 1. We used 36 songsby 12 Japanese singers (6 male and 6 female), each singer sung 3
songs. Each of the songs included only one vocal. The songs weretaken from commercial music CDs that appeared on a well-known
popular music chart2 in Japan and were placed in the top twenty on
weekly charts appearing between 2000 and 2008.
Six recordings pitch-shifted by amounts ranging from −3 to +3
semitones were generated in 1-semitone steps. Since we also used
the original recordings, we had 7 versions of each song and thus used
D = 252(= 7 × 3 songs ×12 singers ) songs for LDA.
Vocal features were extracted from each song (see 2.1), withthe top 15% of feature frames used as reliable vocal frames. The
number of clusters V of the k-means algorithm was set to 100. The
number of topics K was set to 100, and the model parameters of
LDA were trained by using the collapsed Gibbs sampler [46] with
1000 iterations. The hyperparameter α(0) was initially set to 1 andthe hyperparameter β (0) was initially set to 0.1.
1 http://sox.sourceforge.net/
2 http://www.oricon.co.jp/5241

mean rank = 1.56
50
1

reciprocal rankmean reciprocal rank (MRR) R = 0.86

0.5

0M1 M2 M3 M4 M5 M6 F1 F2 F3 F4 F5 F6Singer ID

Fig. 4. The mean reciprocal rank and reciprocal ranks for all songs.

3.1. Experiment A: singer identification
To evaluate the singer identification using the LDA mixing weights
π, experiment A used only the DA = 36(= 12 × 3) songs without
pitch-shifted signals. The left side of Fig. 3 shows a similarity matrix
based on distance calculation using π (eq. 7). The right side of the
figure shows that the similarities of top three similar songs of eachsong are filled with black color.
Figure 4 shows the mean reciprocal rank R defined as follows:
1  1
× 100
DA
rd
DA

R=

(9)

d=1

The mean reciprocal rank is the average of the reciprocal ranks ofresults for DA queries, where rd indicates the rank of song d decided
from the similarity. If a same singer’s song has the highest similarity,
the rank is 1.
These results suggest that songs by the same singer have similar
topic weights, and the topic weights can be used to identify singers.
3.2. Experiment B: cross-gender similarity
To evaluate the cross-gender similarity estimation using the LDA
mixing weights π, experiment B used all 252 songs. Table 2 showsthat a singer ID of the highest similarity song of each query andthese values of pitch-shifted. The mixing weights of the 36 original
songs without pitch-shifting were used as queries, and the retrieval
targets were 245 songs (= 252−7: excluding 7 versions of oneself).
Figure 5 shows numbers of singers who sang the highest similar songof each query. The mixing weights of the all 252 songs were used as
queries.

Most similar song for each query
query 1
query 2
query 3F4 (−3)F4 (−3)F6 (−3)M1 (−2)M3 (+1)
M3 (+1)M2 (+1)M2 (±0)M6 (−1)F6 (−3)F5 (−3)F1 (−3)M3 (+2)F1 (−3)M2 (−1)F3 (−3)M3 (+1)F3 (−3)F6 (+1)F5 (+1)F5 (+2)F6 (±0)F6 (+1)F6 (+1)
M6 (+3)
M6 (+3)
M6 (+3)F5 (+3)F4 (±0)F6 (±0)
M6 (+3)
M6 (+2)F2 (−2)F2 (−2)F5 (+2)F4 (+1)

mixing weight

Queries(±0/×1)
M1
M2
M3
M4
M5
M6
F1
F2
F3
F4
F5
F6

mixing weight

Hirai Ken (M6) / “HitomiWoTojite” (±0 semitone）

Table 2. The highest similarity song of each query, and these valuesof pitch-shifted (experiment B). The “+1” means pitch-shifting upby 1 semitone. The underline means the most similar songs are sungby the opposite gender (M6 and F3).

0.3
38

0.2

83

0.1
0

0.3
0.2

20

40

60

80

100
topic

Hitoto Yo (F3) / “MoraiNaki” (-3 semitones）
38
83

0.1
0
20

40

60

80

100
topic

Fig. 6. Mixing weights of the similar song pair, Hirai Ken (M6) and
Hitoto Yo (F3, 3 semitones lower). The topic 38 is high weight in
both, and the topic 83 is high weight with only M6.
Singer cloud of topic 38

Singer cloud of topic 83

Fig. 7. Examples of topic visualization by the singer cloud. Topic
38 is high weight with both Hirai Ken (M6) and Hitoto Yo (F3), andtopic 83 is high weight with only M6, as shown in Fig. 6.

hen (F4) and GLAY (M4). Even though these two topics are sharedby Hirai Ken, we found that they represent different factors of his
singing voices.
Fig. 5. Number of singers of the highest similarity song of eachquery (252 queries).

These results show that Hirai Ken (M6) and Hitoto Yo (F3) aresimilar when pitch-shifted by 3 semitones. In fact, they are wellknown similar singers when pitch-shifted by 3 semitones. This suggests that the proposed method work well for the estimation of crossgender similarity.
Figure 6 shows the mixing weights of a song “HitomiWoTojite”
sung by Hirai Ken (M6) and its most similar song “MoraiNaki” sungby Hitoto Yo (F3) 3 semitones lower. The figure shows both song
have high topic weights of topic 38 (the cluster number of the kmeans algorithm).
3.3. Singer cloud
Figure 7 shows the singer clouds of topic 38 and 83. Topic 38 is highweight with both Hirai Ken (M6) and Hitoto Yo (F3), and topic 83
is high weight with only Hirai Ken (M6), as shown in Fig. 6. The
size of each singer’s name is defined by summing the same song’s 7
mixing weights (i.e., there are three names of each singer).
The results suggest that topic 38 has characteristics shared byHirai Ken (M6), Hitoto Yo (F3) and Utada Hikaru (F5), and that
topic 83 has characteristics shared by Hirai Ken (M6), Tokyo Ji-

5242

4. CONCLUSIONS AND FUTURE WORKThis paper describes a vocal timbre analysis method based on latent Dirichlet allocation (LDA) where each song is represented as aweighted mixture of multiple topics that are shared by all singing
voices. The paper also describes a method for estimating crossgender vocal timbre similarity. While previous MIR works focusedon retrieving only existing music, our MIR based on this crossgender similarity can find songs whose pitch-shifted singing voices
are similar to a query song. The experimental results showed thatthe mixing weights of LDA can be used for singer identification (see3.1), cross-gender similarity estimation (see 3.2), and singer-cloudsemantic visualization (see 3.3).
Since this paper focused on vocal timbre features, we plan to use
F0 information or other singing features as the next step. The future
work will also include the use of a probabilistic topic model basedon LDA [35, 47, 48] and a nonparametric Bayesian approach [48].
5. ACKNOWLEDGMENTS
This research was supported in part by OngaCrest, CREST, JST. The
work reported in this paper used the Songle modules of Hiromasa
Fujihara to estimate vocal LPMCC and ΔF0 from polyphonic audio
signals. We thank Masahiro Hamasaki and Keisuke Ishida for theirvaluable advice to create the singer cloud.

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