The 21st International Society for Music Information Retrieval Conference (ISMIR 2020)

A Chorus-Section Detection Methodfor Lyrics Text
Kento Watanabe and Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST)

1

Background■ Previous study• Chorus-section detection using audio signals.
INPUT

OUTPUT

Chorus-section
detector
for audio signals

chorus■ This study• Whether chorus sections can be detected from text-only lyrics is an open issue.
• We propose a supervised model that can detect chorus sections in English and Japanese lyrics.
OUTPUTINPUT
1: ooo I wanna kiss you
2: loving you is my dream tonight
3: ooo hold me tenderly
4: loving me with all your heart
5: boy you never tell me that you love me
6: I'm going crazy wondering about you baby
7: do you really know boy how much I care
8: could you really show me how deep is your love?
9: just close you eyes and hear my heart
10: the sweet sweet beat of my love
11: can't you tell I'm hungry baby
12: for only you can make me smile
13: ooo I wanna kiss you
14: loving you is my dream tonight
15: ooo hold me tenderly
16: loving me with all your heartchorus (1)chorus (1)chorus (1)chorus (1)not-chorus (0)not-chorus (0)not-chorus (0)not-chorus (0)not-chorus (0)not-chorus (0)not-chorus (0)not-chorus (0)chorus (1)chorus (1)chorus (1)chorus (1)Chorus-section
detector
for lyrics text

1: ooo I wanna kiss you
2: loving you is my dream tonight
3: ooo hold me tenderly
4: loving me with all your heart
5: boy you never tell me that you love me
6: I'm going crazy wondering about you baby
7: do you really know boy how much I care
8: could you really show me how deep is your love?
9: just close you eyes and hear my heart
10: the sweet sweet beat of my love
11: can't you tell I'm hungry baby
12: for only you can make me smile
13: ooo I wanna kiss you
14: loving you is my dream tonight
15: ooo hold me tenderly
16: loving me with all your heart

2

Potential Application■ A lyrics search systemI want to findlyrics with a chorus
section having aphrase “I love you”.

Lyrics A
chorus

Lyrics C… I miss you …

…

… I love you …

chorus

Lyrics A

chorus

Lyricssearchsystemzzzzz

Lyrics B… I love you …

Lyrics C

Lyrics Database

Listener

It is necessary for a lyrics search system to automaticallydetect which lines of the lyrics are included in chorus sections.
3

arity measure.
ormalized Levenshtein edits of two lines.
• We compute
the Self-similarity
matrix (SSM) from lyrics text.
ormalized
Levenshtein
edit• SSM representations are widely used in computational music structure analysis.
s of the first two words of

Key Idea

Convolutional Neural Network to vectorize
ormalized Levenshtein editrs of the last two words of
Repeated sections lead tohigh values in diagonalsof the matrix, and thosepatterns are used to
identify the structure.

hitecture,as thesameinastheFell et al, which allows): To capture
rhymesto extract
translation, scaling, and rotadnetwork
Levenshtein
edit distanceant featuresption
of two anywherelines. In on
thisthe input image. Oures
the same
motivation
to capture chorus sections
ciationdictionary
to extractus
SSMexample
patterns,
s. Fortheregardlessphonetic of SSM location and4ze.
you” is [AY1, L, AH1, V, Y,
Figure 3. Convolutional Neural Network to
NN structure is illustrated in Figure
3. After theSMMs.
calculated,
we extract
fixed window sub-matricesmsimilaripos ): To capturenwethetargetline:
a normalized•calculate
The designof the similarity measure to compute each cell of the SSM is important.
2w⇥T
(CNN)
architecture,
as
the
same
as
Fell
et
al,
whi
We
proposetouse
thefollowingnine
variationsofsimilarity
measures.
SSM
[t
w
+
1,
...,
t
+
w]
2
R
,
where
w
een •thesequenceofpart-ofmthe neuralnetwork
to extract translation, scaling
window size. The input of the CNN
is nineEdit
sub-distance① String
similaritytt
2w⇥T ⇥9tion
invariantfeaturesanywhere on the input im
S
,
...,
S
}
2
R
,
where
the
number):To
capturethesemantic
2vstr
syL② Head similarity❶❷❸
studykernel
sharessize
motivation to capture choru③theTailsamesimilarity
ndicatethe number
channels. The
we simply
averageofwords’④ Part-of-speech similarity
fromvarious
SSM patterns, regardless of SSM loc
convolutionalis (w + 1) ⇥ (w + 1) so that
⑤ Phonetic similarity
ained
word2veclayer
embeddings
relativesize. Inre can capture a prospective
section.
for identifying the type ofchorus
similarity measure.
ty.
Cosine similarity
String❺similarity (sim ): ❻a normalized Levenshtein edit
❹
This
CNN
structure is illustrated in Figure 3.
distance
between thecharacters of twolines.
rk,
allconvolutional
layersemploybatch
normalm):The
averageofword⑥ Word vector similarity
c2v
Prefix similarity (sim ): a normalized Levenshtein editarewe extract
similarity fixed window sub
distance between
the characters SSMs
ofthe first twowordscalculated,of⑦ Context vector
the ReLU
activation
function.
Eachresultingad “bagof words”(i.e.,
thetwo lines.
Suffix similarity (sim ): a normalized
Levenshteinon
edit the target line:
centeredownsampled
max-pooling
with
w
⇥
w
kernelperson” andby“personbites
time warping
distance between the characters of the last two words Dynamic
of
t
2w⇥T
two lines.
❼
❽
❾
S
=
SSM
[t
w
+
1,
...,
t
+
w]
2
R
⑧
Word
syllablecountsimilaritympooling, we
use similarity
a dropout(p =mrhymess max
assumption).
To Phonetic
consider(sim):layer
To capturein the0.1)
lyrics, we calculate a normalized Levenshtein edit distance⑨ Lyric line syllable count similarity
a fixed window size. The input of the CNN isrization.
webetween
apply
thetranscription
1Disconvolutionalthephoneticof two lines. In thisines usingThen,
pre-trained
con5
study, we used the CMU pronunciation dictionary to extract tt
2w⇥T ⇥9{Sstr , ..., SsyL } 2 R, where th
the phonetictranscription of lines.matrices
For example
the phonetic
a2vec,
kernelsize ofw anddownsample
bythevectorwhich
encodeslines
transcription of the line “I love you” is [AY1, L, AH1, V, Y,
Figure 3. Convolutional Neural Network to vectorize* [Fell et al. 2018]
ofSSMs
indicatethenumberofchannels.
The k
UW1].
SMMs.
annel
thec2vlastusingmax-poolinglayer.
We
perform
ilarityinsim
cosinePart-of-speech similarity (sim ): To capture similarities in grammatical structure, we
calculatea firstnormalizedoftheis (w + 1) ⇥ (w +procedure independently
for
each
line
xt convolutionaland(CNN) architecture, as layer
the same as Fell et al, which allows
Levenshtein edit distance between the sequence of part-ofthe neural network to extract translation, scaling, and rotaspeech of two lines.
eachfeaturecancaptureaCNNprospective
chorussetion invariant
featuresanywhereon theinput image. Our
featurevectorv.
To calculatefeaturevectors
from
the
above
nine
SSMs,
we
use
a
architecture*toWord
vector for
similarity
(sim
): To
capturethe semantictyCNN-based
(sim
):
As
a
clue
syW
study shares the same motivation to capture chorus sections
similarity
between two lines,
we simply
average patterns
words’
detect textual macro
structuresfromvariousinvarious
SSMs
regardlesslayersoftheiremploy
locationsthis
network,allbatcfrom convolutional
SSM patterns, regardlessof SSM location
andvectors
of
each
line
using
pre-trained
word2vec
embeddings
use and
therelative
sequence
ofcomputeword
sizesandin
SSMs.
relative size.
its cosine similarity.
CNN structure
is illustrated in Figure
3. After the
ReLU
activation
function.
Each
Context vector similarity (sim ization): The averageandof word the This
For
example,wordvector
syllableuisticFeaturesSSMs
are calculated, we{"extract
Nine
SSMs
Sub-matrices, …fixed, " window} ∈ ℝ sub-matricesbyword2vecassumes a “bag of words” (i.e., thecentered on the target
line: max-pooling with w ⇥tensorisdownsampledbydifference between “dog bites person”
and “person
bites
S = SSM [t w + 1, ..., t + w] 2 R, where w
es you lost yourself
away”
dog” cannot
be captured in this assumption). To consider
is a fixed window size. The input of the CNN is nine subword
order, towe vectorize
the lines
usingchorus
pre-trainedconuistic
expressions
tend
appearinrelasize.
Aftermaxpooling,
we use, wherea dropoutlayer{S , ..., S } 2 R
the number
text2vec,
an extension
our eyes” are {2, 1,
1, 2,1} of word2vec, which encodes lines matrices
of SSMs indicate the number of channels. The kernel size
using LSTM. Welyric
compute similarity
simusingchorus
cosine
For
data
with
for
regularization.
Then,layerwe
the
1 example, analyzingof the first convolutionalis (w apply+ 1) ⇥ (w + 1)
so that 1D conv
similarity.
y ; lines that haveWord
a similar
each feature can capture a prospective chorus section. In
syllable count similarity (sim): As a clue forwephrases
about the layer
futuresuch
asthis network,size
witha kernelof w layers
andemploydownsample
byall convolutional
batch normaldetecting chorus sections, we use
the sequenceof wordkelyfoundto be thatthe same
sectionization and the ReLU activation function. Each resulting
syllable counts on each line. For example, word2D-convolution
syllableis downsampled
by max-poolingwith w ⇥ w kernellayer. W
extract
fixed
t’s”
and
“I
wanna”
tend
toappearthechorus+ ReLUtensorin
of
each
the
last
max-poolingcountsof two lines“Sometimes
youin
lost
yourselfchannel
away”
max
window sub-matrix
size. After max pooling, we use a dropoutpoolinglayer (p = 0.1)
se Dynamic Time andWarping“Everytime you just close your eyes” are {2, 1, 1, 2, 1}
for regularization. Then, we apply the 1D convolutionaland{2, 1, past
1, 1, 1, 1} respectively; linesthathave a similarhilephrases
about
the
such
as
“have
been”
the
above
procedure
independently
for
each
lin
layer
with
a
kernel
size
of
w
and
downsample
bythevectorrity between sequencesof are likely to be the same section of each channel in the last max-pooling layer. We perform
syllable count sequence
inlessthe song.in
In thisstudy,chorus
we useobtain
Dynamic
Timethe
Warping’t”
tend
toappearthesection.
To
CNN-basedfeaturet.
the above procedure
independentlyvector
for each line v
x and
(DTW)
to
calculatethesimilarity
betweensequencesofle count sequences.
obtain the CNN-based feature vector v .
different lengths, such as syllable count sequences.
ch a linguistic expressionofthechorussection,syllableusecount similarity (sim ): We can also use(simsyL ): We canLine
also
max pooling
3.2 Linguistic Features
1D-convolution
the sum of the syllable counts of all words in each line.
e two linguistic features.
+ ReLUFor example, in the lyrics of Figure
1, thetotal syllable
3.2
Linguistic
FeaturesSomelinguistic
expressions
tend to appear in chorus relaof all words in eachline.
count of thefirst line in the chorus section is all six, buttionships. For example, analyzing lyric data with chorus
the total syllable
countWein the second
line
is eight. For- offword
vectors
(word
):
use
averageavemarkup, we found that phrases about the future such as
Figure 1, the totalmally,syllable
we calculated the similarity of the total syllable“I’ll”,expressions“Let’s” and “I wanna” tend
to appearto
in theappear
chorusSomelinguistic
tend
in ch
count
of eachline
in the following
procedure.
(1)
We study,
shift
vectors
of
a
given
line
as
features.
In
this
section, while phrases about the past such as “have been”
ofbut
four lines over lyrics and extract four lines
horus section is allaL window
six,
and “didn’t” tend to appear less in the chorus section. To
Forexample, analyzing lyric data wi= {x , x , x , x }. (2)tionships.
The similarity between
exploit such a linguistic expression of the chorus section,esecondof wordvectorsusing
pre-trained
word2vec,the line x and
x is calculated by the DTW of L and L .
line is eight.
Forwe propose two linguistic features.
We calculated nine self-similarity
matrices SSMwe
2 foundmarkup,thatphrasesthe
futur
Average of word
vectors(word ): Weabout
use average of
ut-of-vocabulary
words.
, where each cell is a similarity measure as described
the word vectors of a given line as features. In this study,
larity ofChorus-section
the totalRabove.
syllableThen,Non-chorus-section
to calculate feature
vectors
from
the
above
■
Findings“I’ll”,(word“Let’s”
and“Iof word
wanna”
appear in t
theaverage
vectors using tend
pre-trainedto
word2vec,
presentationsforword
sequences):nine
SSMs, we exploit
the Convolutional Neural
Networkseq
skipping out-of-vocabulary words.
Tri-gram P(1)PWe
Tri-gramPn Pc
cning procedure.
shift
y word
ysuch
yt
t and
Phrasesabout
thefuture(e.g.,
I’ll
Vectorrepresentations
for t-1
sequences(word):
section,while
phrases
about
the
past
as
“h
I’mbe modeled
0.04%of worder cannot
bythere’s
the average…cannotto0:not-chorus
Word orderbemodeled inby theaverageof word
1:chorus
1:chLet's)
tend
appearchorussections
yrics
anddon’t
extract0.12%
four
lines
0.11%
I’ve
and0.03%“didn’t” tend
to appear
inabout
the chorussemore often
than do lessphrases
the
oh oh
0.05%
0.03%
past (e.g., have been
anddidn't). h [y ](2) Theohsimilarity
between’s a
h
[y
]
h [
exploitsuchalinguistic
expression
of
the
choru
I’ll
0.05%
I’d
0.02%
0
by the DTW
Lt and Ltbut. I’ we0.02%
we’re of0.04%
propose two linguisticfeatures.
LSTM
LSTM
…
’s not0.01%
milarity you’rematrices0.04%
SSMm 2
LSTM
LSTM
LSTM
It
can
be
expectedthatsomeexpressions
will
Average’ll be0.04%
what’s
0.01% of word vectors (wordave ): We use acontribute to the chorus-section detection.
ilarity measureasdescribed
I don’
0.04%
na na na the0.01%
word vectors of a given line as features. In t
Let’s from0.03%
yeah yeah yeah
0.01%
ure vectors
the above
average of word vectors
using
pre-trainedw
Concatenation
you got ta
0.03%’ve been the0.01%
ut
vt
volutional
Network
I can’Neural
0.03%’t take
0.01% out-of-vocabulary words. Averaging
skipping

Nine SSMs

str

pre

suf

phone

Structural Feature
pos

w2v

c2v

$
#$%

t
m

$
#()

-.×0×1

2w⇥T

m

t
str

2w⇥T ⇥9

t
syL

c2v

syW

1

…

…

Kernel size:t

Kernel sizet

syLSome expressions of lyrics tend to
appear in chorus sections?
t+2

t+3

t0

t

t0

t

m

ave

Bi-LSTM Layer Label

T ⇥T

1 The song is from the RWC Music Database (RWC-MDB-P-2001

0.03%

didn’t

0.01%

seq

t-1

t-1

t

t

t+1

CNN
…
Vector representations for word sequences (Pc and P(RWC-MDB-P-2001
n denote word tri-gram probabilities in the chorusc Database

LSTM

can’t

Feature Vector

No.92) [3].

LSTM

t+1

LSTM

t

LSTM

t

sections,
respectively.
Tableand1. non-chorus
Frequent word
tri-grams
in chorus and non-chorus
Word order cannot be modeled by the average
7
sections. An apostrophe is regarded as a word.
loving you is
my drLine xt

Sub-matrices

Chorus-section Detection Model

word tri-gram probabilities in the chorus and non-chorus
Figure 3. Neural-network-based sequence
sections. Table 1 shows the word tri-grams that frequently
for chorus-section detection.
appear
in
both
of
the
sections.
Here,
P
and
P
denotecn using two types of feature representations.
• We propose a Bidirectional LSTM-based
model
word tri-gram probabilities in the chorus and non-chorus
4. EXPERIMENT
sections, respectively. As shown in this table, we found thatphrases about the future (e.g., “I’ll” and “Let’s”) tend to
Inspired by audio-based chorus-section det
appear in chorus sections more often than do phrases about
evaluated the proposed method by using thethe past (e.g., “have been” and “didn’t”). To exploit suchthat is a harmonic mean of precision (P )tendencies, we propose two linguistic features.
F = (2 · R · P )/(R + P ), where
Average of word vectors (lingave ): We use the average of# of lyric lines in correctly detected cho
P=# of lyric lines in detected chorus s
word vectors of a given lyric line as a feature. The word
vectors are obtained using pre-trained word2vec [42], skiplyric lines in correctly detected ch
R = ##ofoflyric
lines in correct (annotated) chping out-of-vocabulary words.
We also used the pair-wise F-measure (p-F
Vector representations for word sequences (lingseq ): To
conditional entropy F-measure (n-F ) andconsider the word order that cannot be modeled by lingave ,that areprovided by the Python module m
we use pre-trained
context2vec[43] that enables vectoriza-Linguistic
Features
Structural
Featurecommonlyused to evaluate
computationalmtion of a lyric
byaputting
a sequenceof word vectorsOur modelutilizes linguisticfeatures suchas
We lineexploit
CNN architecture
to detectword vectors
and[51].
sentence vectors.
textual macro structures from nine SSMs.
analysis
into the LSTM.
8
We calculate the above linguistic feature vectors for each
4.1 Methods Comparedlyric line xt and obtain their concatenated vector ut .

Training Data

To confirm the effectiveness of our Bi-LSTM
on the Bi-LSTM model that can learn detween adjacent lyric lines, we compared it
To solve
the sequence labeling problem, we use the stan■ Technical Problem for training our modelwith that of with two baseline methods:
dard Bidirectional Long Short-Term Memory (Bi-LSTM)
No lyrics data with line-level chorus-section annotations
are available.
(1) Heuristic:
We implemented the heurist
networks[49] to compute the conditional probability
at the end of the lyrics are repeated with s
P (Ys |Xs ). The neural network structure is illustrated in■ Keyannotation
tions, all those
repeated lines are chorus se
Figure3. Idea： We generate training data with chorus-section
Weto
prepared
100,772
pairs
of musicalsignalsfollowing
and theirprocedure:
corresponding
manually(i) From
the SSM tha
The 1.
input
the Bi-LSTM
layer
at eachtime stepaudiot (lyric
time-aligned (temporally synchronized) lyrics.
of the nine SSMs, we extracted diagonals wline) is a concatenation of two different types of feature
2. We detected chorus sections of every song automatically by using its audio signals.
values higher than a threshold , which w
vectors: (1) structural feature vectors v t encoded from
3. If the start time of a lyric line was within any chorus section detected in audio signals,
development
set to be = 0.62. (ii) From t
nine variationsof SSMs
in Section
3.1 otherwise,and (2) linguisticthat line
was labeledchorus;it was labeled
not-chorus.
agonals, we selected
the shortest diagonal amfeature vectors v t encoded in Section 3.2. Formally, thechoruschorusplaced at thebottomof the SSM (e.g., the diconditional probability P (Ys |Xs ) is calculated by using a⑪⑫ ⑬ ⑭ ⑮ ⑯ ⑰ ⑱⑪⑫ ⑬ ⑭ ⑮ ⑯ ⑰ ⑱⑪⑫ ⑬ ⑭ ⑮ ⑯ ⑰ ⑱
at the cell SSM[29; 1] in Figure 1). (iii) S
softmax function:corresponding to the rows where the selectes ,Ys ))P (Ys |Xs ) = P exp(Score(X.
(1)
0
Ys0 exp(Score(Xs ,Ys ))located (e.g., lyric lines 29–32 in Figure 1)
Synchronized audio and lyrics
2. Chorus-section detection from audio
3. Eachchorus.
line is labeledchorus/not-chorus
the label(iv)
Other successive
The 1.
Score()
is defined assimilar to the chorus lines (e.g., lyric linesX
9
Score(Xs , Ys ) =BN(ht [yt ]),(2)
in Figure 1) were also assigned chorus lat(2) Multi-Layer Perceptron (MLP): Like
where ht [yt ] is the output of the Bi-LSTM for each time
method, but with the Bi-LSTM model repl
step t and BN() denotes batch normalization [50]. In thedard MLP model. This method ignores transmodel training step, we use a binary cross-entropy loss.
adjacent lyric lines and predicts yt from xt
3.3 Neural Network-based Sequence Labeling Model

us
or
ch

ly
on

u
yo

…

…

…
do
:
t
18 n'
ca
us
:
or
17
ch
…
t- us
ng
no or
vi
ch
lo
t…
no
6:
o
1
…
…
oo
us
ng
5:
or
vi
ch s 1lo …
u:or
14 ooo
ch
…
us :
or
or 13
f
ch s
:
u
12
…
or
t
ch
us
n'
or
ca
ch
:
11

tno

tno

r
fo

t
n'
ca

ly
on

u
yo

…
u
yo
…
u
do
yo
:
t
18
n'
ca
…
:
g
n
17
vi d …
l
lo
ho
:
16 oo
…
o
:
ng
15
vi …
lo
I
:
14 ooo
…
:
13

:
12

:
11

r
fo

t
n'
ca

…
u
yo
…
u
do
yo
:
18
't
n
ca
…
:
g
n
17
vi d …
l
lo
ho
:
16 oo
…
o
:
ng
15
vi …
lo
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:
14 ooo
…
:
13

:
12

:
11

Dataset■ Dataset for training comparison• EN_auto: English lyrics data with the generated chorus-section annotations. (9,313 songs)• JA_auto: Japanese lyrics data with the generated chorus-section annotations. (91,459 songs)• JA_man: Japanese lyrics data with the gold chorus-section annotations.
(1,103 songs)■ Dataset for tuning model parameters
We annotated chorus-section label manually.
• EN_RWC: English lyrics data in RWC Music Database. (21 songs)• JA_RWC: Japanese lyrics data in RWC Music Database. (79 songs)■ Dataset for testing
We annotated chorus-section label manually.
• EN_test: English lyrics data.
(118 songs)• JA_test: Japanese lyrics data. (128 songs)
10

Experimental Results (1)■ Question
To confirm the effectiveness of our Bi-LSTM model that can learn dependencies between
adjacent lyric lines, we compared its performance with that of two baseline methods.

■ Result
Training and test data (# of training songs)
MethodEnglish (9,313 songs)
F-measureJapanese (91,459 songs)
F-measure

Heuristic(extract repeated lines as chorus sections)57.8 %

57.1 %

Multi-Layer Perceptron

74.2 %

80.6 %

Bi-LSTM (Proposed)

78.1 %

83.4 %■ Findings• Methods based on supervised learning are better than a rule-based method.
• The proposed method is the best for the chorus-section detection task.
11

Experimental Results (2)■ Question
To investigate the effectiveness of structural and linguistic features, we compared theiruse individually and in combination.

■ Result
Training and test data (# of training songs)
FeatureEnglish (9,313 songs)
F-measureJapanese (91,459 songs)
F-measure

Structural feature

77.9 %

81.2 %

Linguistic feature

57.4 %

55.2 %

Both

78.1 %

83.4 %■ Findings• The model with only the structural features greatly outperformed.
• The additional use of linguistic features is helpful for detecting chorus sections.
12

Experimental Results (3)■ Question
We confirm that our generated data is reliable enough for training purposes by comparing
the performance of the model trained on JA_auto with that of the model trained on JA_man.

■ Result
Training Data

F-measure (Japanese test data)

JA_auto: generated training data (91,459 songs)

83.4 %

JA_man: human-annotated training data (1,103 songs)

80.3 %■ Findings• The model trained using generated data (91,459 songs) outperformed themodel trained using human-annotated data (1,103 songs).
• Even if generated annotations are not perfect, they are reliable enough fortraining.
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Experimental Results (4)■ Question• Can a model trained on a large amount of Japanese data detect English chorus sections?
• Can a model trained on both EN_auto and JA_auto perform better than one trained on only
EN_auto or JA_auto?
★ Structural features based on the SSMs can be language independent because our SSMs simply
represent patterns of repeating lyric lines, which could be universal in music. So we use
the model without linguistic features in this experiment.

■ Result

Training Data
EN_auto: generated training English data (9,313 songs)
JA_auto: generated training Japanese data (91,459 songs)
EJ_auto: EN_auto + JA_auto (100,772 songs)

F-measure (English test data)
77.9 %
80.3 %
81.0 %■ Findings• The SSM-based model can detect chorus sections regardless of the language.
• English and Japanese SSMs (i.e., patterns of repeating lyric lines) have similar structures.
• Mixing different language data allows the model to learn the general structure of chorus
sections and thereby perform better.
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Conclusion■ Contributions
1. We designed a variety of features to capture structural and linguistic
properties of chorus sections.
2. We proposed a sequence labeling model that can detect chorus sections in lyrics.
3. We showed how to generate a large training dataset of lyrics with chorussection annotations.
4. We demonstrated that our Bi-LSTM-based method outperforms alternative
baseline methods.
5. We thoroughly investigated this detection task and the nature of chorus sectionsof lyrics from different perspectives such as the importance of features, theamount of training data, and language dependency.

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