ANALYSIS AND DETECTION OF SINGING TECHNIQUES IN
REPERTOIRES OF J-POP SOLO SINGERS
Yuya Yamamoto1
Juhan Nam2
Hiroko Terasawa1
1
Doctoral Program in Informatics, University of Tsukuba, Japan
2
Graduate School of Culture Technology, KAIST, South Korea
s2130507@s.tsukuba.ac.jp, juhan.nam@kaist.ac.kr, terasawa@slis.tsukuba.ac.jp

ABSTRACTIn this paper, we focus on singing techniques within thescope of music information retrieval research. We investigate how singers use singing techniques using real-world
recordings of famous solo singers in Japanese popular music songs (J-POP). First, we built a new dataset of singing
techniques. The dataset consists of 168 commercial J-POP
songs, and each song is annotated using various singing
techniques with timestamps and vocal pitch contours. We
also present descriptive statistics of singing techniques on
the dataset to clarify what and how often singing techniques appear. We further explored the difficulty of the automatic detection of singing techniques using previously
proposed machine learning techniques. In the detection,
we also investigate the effectiveness of auxiliary information (i.e., pitch and distribution of label duration), not only
providing the baseline. The best result achieves 40.4% at
macro-average F-measure on nine-way multi-class detection. We provide the annotation of the dataset and its detailon the appendix website 0 .
1. INTRODUCTION
A singing voice is one of the most essential elementsof music. It provides impactful emotional expressions
through melody and lyrics. In particular, in popular music, the role of the singing voice is more important, as the
vocal quality and unique style of singers are critical in attracting people. Vocals mainly consist of singer individuality (i.e., vocal fold vibration and vocal tract resonance)and singing expressions (i.e., fine control of pitch, timbre,and loudness). Singing techniques, as the name suggests,
are extended techniques in human singing. It characterizes
singing voices as a component of expressions in vocal performances. In particular, many professional singers in popular music use singing techniques to make a performance
more expressive, each in a different manner.
0 https://yamathcy.github.io/ISMIR2022J-POP/© Yuya Yamamoto, Juhan Nam, and Hiroko Terasawa. Licensed under a Creative Commons Attribution 4.0 International License(CC BY 4.0). Attribution: Yuya Yamamoto, Juhan Nam, and Hiroko
Terasawa, ªAnalysis and detection of singing techniques in repertoires of
J-POP solo singersº, in Proc. of the 23rd Int. Society for Music Information Retrieval Conf., Bengaluru, India, 2022.

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The singing voice is an important subject in the fieldof music information retrieval (MIR), and there are manyprior works that extract quantitative or objective information as in the task of singing transcription [1], lyric
identification [2], and singer identification [2]. Computational analysis of singing techniques based on MIR cancontribute to the clarification of singing styles and canbe applied to music discovery, vocal training, consumergenerated media and so on. Singing techniques have beenof keen interest, especially in J-POP, both among singers
and music creators; the singing style of J-POP singers has
a wide variety, and their singing techniques are diverse. In
addition, singing techniques are one of the evaluation criteria in Japanese commercial karaoke systems with scoring systems. Meanwhile, as for music creators, VOCALO
songs, whose singer’s voice are created by a singing voicesynthesis software such as VOCALOID [3], have been established as a music genre in Japan. Many VOCALOID
creators manually manipulate the voice of VOCALOID
to make it more expressive, sometimes while referring to
how the actual singer produce the singing voice, like how
singing techniques are applied. Therefore, J-POP is an attractive research target for singing technique analysis andcomputational singing technique analysis may bring benefits to real-world applications.
In this regard, we present a case study of singing technique analysis in J-POP. We first introduce a new dataset,including the annotation of singing techniques for J-POP
recordings. We analyze the dataset using descriptive statistics to illustrate the technique distributions and singer
styles. Finally, we conduct singing technique detection
based on machine learning methods, which can be beneficial to analyze the one side of the singing voice automatically. We demonstrate the detection of various singing
techniques (i.e., identifying the singing technique typewith its starting time and duration) and report baseline results using existing methods. Finally, we investigate the effect of the identification model with auxiliary information
derived from the statistics of the dataset and pitch information.
2. RELATED WORKS
2.1 Singing Technique Analysis
Analyzing the acoustic patterns of singing expressions has
been a long-standing research topic. Research on vibratoProceedings of the 23rd ISMIR Conference, Bengaluru, India, December 4-8, 2022

dates to the 1930s [4]. Later, computer-based methods allowed a more quantitative analysis of vibrato parameters[5,6]. In addition to vibrato, other singing techniques, suchas growling [7], portamento [8], voice registers [9,10], andextreme vocal effects [11] have also been investigated. An
analysis of singing techniques was also conducted for JPOP. Migita et al. investigated the vibrato parameters of
the imitated singing voices of J-POP vocalists [12]. Yamamoto et al. conducted an exploratory analysis of singing
techniques that imitate the singing style of J-POP singers[13]. They found 13 types of singing techniques from48 tracks and analyzed them with annotations. Because
singing techniques in J-POP cover wide repertoires [14],such exploratory analysis is also important for the purposeof singing performance analysis. As our interests are handling and detecting multiple singing techniques rather thanspecifying a single technique, we annotated the label asin the manner in [13] but on the original real-world tracksof J-POP. Therefore, our new dataset consists of labels for
multiple singing techniques.
2.2 Singing Technique Datasets
There are a handful of datasets that focus on the analysis of
singing techniques. The Phonation Mode dataset consistedof four vocal modes (neutral, pressed, breathy, and flow)of sustained sung vowels from four singers [15]. Although
the dataset includes a wide range of pitches, they are discrete, and thus lack a melodic context. VocalSet [16] handles the issue by having voices sung in contexts of scales,
arpeggios, long tones, and excerpts. In addition, it covers abroader range of singing techniques, such as vibrato, trill,
vocal fry, and inhaled singing. Because audio samples inthe two datasets were newly recorded, they were collectedunder controlled conditions. Therefore, their characteristics of singing techniques might be different from those
appeared in songs.
However, several datasets have been annotated for realworld vocal music. The KVT dataset was originallybuilt for vocal-related music-tagging tasks in K-POP music [17]. It contains 70 vocal tags, of which six are related
to the singing technique (whisper/quiet, vibrato, shouty,
falsetto, speech-like, and non-breathy). The annotation
was conducted using crowdsourcing. The MVD dataset
was built to analyze screams in heavy metal music, andhas four different types of screams (high fry, mid fry, lowfry, and layered) [18]. The regions and types of screams in
the audio files were manually annotated. Similar to thesestudies, we are interested in versatile singing techniques inreal-world music and thus we chose commercial music tobuild the dataset.
2.3 Singing/Playing Technique Identification on MIR
research
Studies have been conducted on the automatic identification of singing techniques beyond computational analysis. There are two scenarios for identifying a singing techniques. One is classifying sung vocal audio into singing

techniques. Several studies have conducted singing technique classification on VocalSet [19] and phonation modes[20, 21]. These works identify singing techniques but do
not provide time-related information, such as start time andduration. The other method is to detect the singing technique in time. Miryala et al. [22] identified the singing
expressions of raga, classical music in India. They created 35 recordings in eight ragas sung by six singers andshowed a classification accuracy of 84.7% using a rulebased classifier. Yang et al. [8] proposed AVA, an interface for analyzing vibrato and portamento based on thefilter diagonalization method (FDM) and hidden Markov
model (HMM), respectively. They also provided a casestudy of the analysis of vibrato and portamento in the Beijing opera. Ikemiya et al. [23] provided rule-based parameterization of four singing expressions (vibrato, kobushi,
gliss-up, gliss-down), extracted them from recorded vocal tracks to transfer to other vocal tracks, and demonstrated them on two sung excerpts. Kalbag et al. [18]provided scream detection for heavy metal music. They
also built the MVD dataset and demonstrated classification, including non-scream singing and non-vocal music.
Since singing techniques appear locally on sung vocals, we
adapted these temporal detection strategies for identification.

3. DATASETIn this section, we describe the organization of our new
dataset used for the analysis of this study. Due to the limitof the space, more details are described in the appendix
site 0 .

3.1 Song Selection
To cover a wide range of singing techniques, the dataset
should include various types of vocalists, considering gender, genre, tempo, and mood. We first listed 42 solo singers(21 males and 21 females), and four famous hit songs wereselected from each singer to be as different as possible.
Each song was performed as solo performance in Japanese.
We collected audio tracks from commercial CD recordingsof the J-POP songs. We trimmed the collected audio tracks
and annotated only the first consecutive section (i.e., verseA, verse-B and Chorus). Since we prioritized including
various songs in our dataset rather than fully annotating
multiple verses in a single song, we could collect a diverse
set of singing styles.
When using popular commercial songs for academic research, copyright is always an issue. The works by Kim[17] on K-POP and Kalbag [18] on heavy metal music aregood examples of developing datasets using commercialmusic, which distributes only labels about the performed
music but not the audio signals or score information. We
followed their solutions and made our dataset that consistsof singing technique and pitch (frequency) contour labels.
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Figure 1. Singing techniques included in the dataset. (upper) Pitch techniques with a sketch of pitch contour. Gray is thetarget vocal note, the red line is the pitch contour and blue dotted lines are boundary of each technique. (middle) Timbral
techniques. We also show the non-technique extracted from the same track. (lower) Miscellaneous techniques.
Techniquevibratoscooping
drop
bend
hiccupFigure 2. An overview of annotation. (middle) an excerpt
spectrogram with singing technique (colored area) and vocal melody (orange curve).

melismatrill

3.2 Data Pre-processing
The dataset contains two types of annotations: vocal
melody and singing techniques. We illustrate the annotation result in Figure 2. All annotations were conducted
on isolated vocal tracks after vocal separation using Demucs v3 [24], which is a state-of-the-art model for musical source separation. During the annotation process, we
confirmed that there was no dropout of vocal regions bycomparing the original mixed track. Instead, we observedthat the separated vocals tends to retain the back-chorus
or sometimes instrumental sounds that are similar to thesinger’s voice (e.g., electric guitar, synthesizer).

falsetto
breathywhisperrasp
vocal fry
spokenshout
tongue trill

description
a periodic oscillation of pitch.
an upper continuous picth change
a lower continuous picth changea short tremolo or U/inverted-U
shaped pitch changea short hiccupingon attack/release of notea musical arrangementin which several notes areapplied to one syllable of a lyric.
a continuous pitch change
between two notes
sung by falsetto register.
sung by breathy sound.
sung like whispering.
sung by a creaky voicewith subharmonics.
sung by a creaky voice
and pulse register phonation.
singing like rapping, sprechgesang 1 ,and some other styles like speaking.
shouting.
a rolling tongue,
occurred on [r] consonant.

type
pitch
pitch
pitch
pitch
pitch
pitch
pitch
timbretimbretimbretimbretimbre
misc.
misc.
misc.

Table 1. The description of each singing technique appeared in the dataset.

3.3 Singing Technique Annotation
We thoroughly surveyed singing techniques based on instructional books [14, 25] and other conventional scientificresearch related to singing techniques [7, 13, 16, 17, 26±29]
and defined the labels for the major techniques that appear
in these references. We manually annotated songs using
these labels.
Although many other singing techniques still exist, we
1 A vocal style between singing and speaking.
2 Although discarded in the analysis, we also annotate ‘unknown’ labels if the region seems to represent a singing technique but is difficult toclassify them into any of the techniques above and found 24 unknown

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considered the following 15 singing techniques in thisstudy. We show the description of each singing techniquein Table 1. The pitch contour sketches of these techniquesand spectrogram examples are illustrated in Figure 1.
We note that these label names are not unique in thereal-world (e.g., scooping is also called ‘portamento,’ ‘glissando,’ ‘gliss-up,’ ‘shakuri’ (in Japanese)). We madetechniques in total. In the techniques, there are some sounds akin tocoughing and pig squeals, for example.

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Figure 3. Distribution of song year of release.

Figure 4. Distribution of song length in seconds.
frame-level annotations on the audio tracks collected basedon the vocabulary. Singing techniques were carefully annotated by an experienced vocalist (i.e., the first author of
the paper) with the helps of sound playback and visualizing
the spectrograms and pitchgrams. The annotation process
is conducted on Sonic Visualizer [30].

Figure 5. Statistics of the labels. upper: counts, lower:
total duration.

3.4 Melody Annotation
Since pitch is an essential component of singing techniqueanalysis, we further annotated melodic pitch using Tony[31], followed by manual correction such as removing theunvoiced parts and reverberation tails.
4. DESCRIPTIVE STATISTICS
4.1 Song Statistics
The distribution of songs selected for the dataset based on
the year of release is shown in Figure 3. Songs can be collected from various eras, ranging from 1968 to 2021. The
distribution of the songs in the dataset is shown in Figure 4.
The overall length was 4h 47m 39s, and the average lengthof a song track was 1m 43s. The ratio of technique regions
per song track was 22.8%.
4.2 Label Statistics
We present the statistics for the annotated labels as a histogram at the upper side of Figure 5. The most frequent
technique is ‘scooping’. It is followed by ‘vibrato’, ‘bend’,and ‘drop’. This indicates that such techniques are relatively common in J-POP. These techniques are also usedin Japanese commercial karaoke systems for vocal assessment [32]. ‘scooping’, ‘bend’ and ‘drop’ are portamento,
whose frequent use is sometimes considered undesirable inclassical singing [25]. It can be said that this frequent useof portamento is a characteristic of J-POP.
We show the distribution of techniques by each singer
in Figure 6 and find that the occurrence frequency of thetechniques is different for each singer. We also confirmedthat scooping appears more than 29 times for every singer,
whereas several singers tend to not use vibrato frequently(e.g., ‘creephyp’, ‘aimyon’, and ‘yoasobi’).
The lower side of Figure 5 and Figure 7 shows the total
duration and distribution of each technique using a boxen-

Figure 6. Occurrence distribution of singing techniques by
singer. The vertical black line divides each category.
plot, respectively. Figure 7 shows that most of the techniques are relatively short (that is, the range between 0.1s
and 1s.), especially for ‘drops’, ‘scooping’, ‘bend’, ‘hiccup’, and ‘vocal fry’. The average length of the singing
techniques was 0.4s.
5. SINGING TECHNIQUE DETECTION
In this section, we describe the detection of the singing
techniques. We conducted experiments on a multi-classdetection scenario, which handles classification and localization simultaneously. The problem setting is the sameas that in sound event detection (SED) [33]. Figure 8 illustrates the proposed method. We investigated the performance of the CRNN model in a singing technique detec3 https://seaborn.pydata.org/generated/seaborn.boxenplot.html

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Proceedings of the 23rd ISMIR Conference, Bengaluru, India, December 4-8, 2022

Figure 7. Distribution of each technique. Red and blue
horizontal lines indicate 1 s and 0.1 s, respectively.

short-time Fourier transform (STFT), we used a Hann window with 2048 samples to compute the discrete Fouriertransform (DFT). The hop size was set to 10 ms in every
experiment.
We used a CRNN model, which is widely used as abaseline system for SED. We adapted the settings of themodel of Imoto et al. [34], which treated a similar issue(i.e, label sparseness) of ours in SED. The model configuration is shown in Figure 8. The model consists of three
convolutional layers, one layer of bidirectional GRU cells,
and one fully connected (FC) layers. The input was theaforementioned log-mel-spectrogram, and the output was amulti-hot vector representation of the singing technique foreach time frame. All experiments were conducted using abatch size of 16, and the model parameters were optimized
using an Adam optimizer with a learning rate of 10−4 . The
model stopped training if the value of the loss function on
the validation set did not improve over 10 epochs.
The model was optimized using the binary crossentropy (BCE) loss. Performance was evaluated using
segment-based recall (R), precision (P), macro-F-measure(Macro-F), and micro-F-measure (Micro-F) [35] using
sed_eval. The macro-F-measure and micro-F-measure denote the class-wise and instance-wise average of the Fmeasure, respectively. We set the segment length to 50
ms.
5.2 Model Improvement

Figure 8. An overview of the singing technique detection.
The configuration of the CRNN model is also shown.
tion task. In addition to running the simple setting, we also
investigated how the considerations of the characteristicsof the dataset improve the performance (i.e., label sparseness and pitch information). Thus, through the experiment,
we attempted to answer for the following three researchquestions: 1) To what extent can we detect singing techniques using machine learning? 2) Can modification of theloss function treat the problem of label sparseness? 3) Can
auxiliary pitch information help improve the detection performance?
5.1 Experimental Conditions
We performed singer-wise seven-fold cross validation during the experiment. We first split the singers into sevengroups. Then, in each run, one group is left out for test
set, another one group is for validation set , and the rest
are used for training set. Owing to the label imbalancesof techniques between singer, we used the 9 most common
classes (i.e., ‘bend,’ ‘breathy,’ ‘drop,’ ‘falsetto,’ ‘hiccup,’‘rasp,’ ‘scooping,’ ‘vibrato,’ and ‘vocal fry’), which appear
on every fold. We divided the singer fold to balance theamount of each technique as much as possible.
We segmented the vocal tracks, which are separated by
Demucs v3 [24] in advance, into 10s audio clips and nonoverlapping parts at a sample rate of 44.1 kHz. Therefore,
they were converted into 64-dimensional log-mel spectrograms. For experiments that involve the computation of388

5.2.1 Label-frame sparsity
As mentioned in Section 4.2, most singing techniques havea short duration (i.e., shorter than 1 s). This can cause label imbalance between non-technique frames. To alleviate
the problem, we investigated the effect of focal loss [36],which was originally proposed for image object detection.
The focal loss can be represented as follows:
Lf l (pt ) = −α(1 − pt )γ log(pt )(1)where α ∈ [0, 1] is a weighting factor for balancing theimportance of positive and negative examples, and the term
(1 − pt )γ is a modulating factor, with γ controlling the rateof dominant examples. We set α = 0.2 and γ = 2 for all
conditions in the work that used focal loss.
5.2.2 Pitch information
We demonstrated that some of the techniques, such as vibrato and scooping, have a pattern of the shape to certain
extent. Therefore, it is possible that explicitly feeding thepitch helps in the detection. We further investigated theeffect of the auxiliary information. Under this condition,
we considered two ways of obtaining pitch in our experiment. One is the ground-truth (GT) pitch annotation mentioned in Subsection 3.4. However, when used in realworld applications, it is difficult to obtain correct pitch annotation. Hence, we also used pitch estimation predicted
by CREPE [37] on a separated vocal track. As CREPE
can compute pitch confidence, we adopted the pitch value
4 https://tut-arg.github.io/sed_eval/index.html

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BCE
Focal
BCE-GT
BCE-CREPE
Focal-GT
Focal-CREPE

Macro-F
37.7%
39.6%
39.1%
39.1%
40.4%
40.3%

Micro-F
56.6%
57.7%
57.1%
57.1%
58.3%
57.9%

P
40.2%
40.3%
41.4%
40.2%
43.1%
42.7%

R
41.7%
42.6%
42.8%
41.7%
42.2%
43.3%

Table 2. The results of singing technique detection.
where the confidence value was higher than 0.5, whose
overall accuracy was 78.0% evaluated by mir_eval [38].
We converted the pitch contour to a mel-band pitchgramthat has the same frequency dimensions as the input melspectrogram, as in the work of singer identification [39].
Therefore, we stacked it on a mel-spectrogram along thechannel axis.
5.3 Experimental Results
5.3.1 Baseline
The first row of Table 5.3 shows the results of the CRNN
model compiled by BCE: 37.7% for Macro-F and 56.6%
for Micro-F. The fact that Macro-F is much lower than
Micro-F indicates that the model failed to identify minority rather than majority classes. Actually, the class-wise
F-measure, as shown in Figure 9, indicates that detectionof the minority techniques (e.g., ‘rasp’ and ‘vocal fry’) ismore difficult than the majority techniques (e.g., ‘vibrato’and ‘scooping’).

Figure 9. Class-wise F-measuredark-red bars in Figure 9). This indicates that the condition is robust to techniques that have a short duration orsome relationships with pitch.
5.3.3 Challenges
The remaining challenges in the task are detected unnatural region lengths and singers’ identity. We confirmed that
one of the common mis-detection cases is from the detection of too short or frequently switching regions. It mightneed to consider post-processing, such as temporal filteringor probabilistic model (e.g, hidden Marcov model, hidden
semi-Marcov model, etc.). As for singer identity, it can affect the performance of the timbral techniques. We labeled
timbral techniques when the sung voice transformed fromthe ordinary voice of the singer, and it caused the confusion. For example, the ‘rasp’ from a singer who has clean
voice and ordinary voice from a singer who has raspy voice
can be confusing. Therefore, there is still an issue of disentangling singers’ identity and singing techniques.

5.3.2 Effect of focal loss and pitch information
First, we show the results for the effect of focal loss in the
middle part of Table 5.3. The detection performance improved by 1.9% in Macro-F. We further studied the classwise F-measure, as shown in Figure 9, and confirmedthat the performance of short techniques, such as ‘bend’(38.0% → 41.1%), ‘drop’ (35.7% → 39.3%), and ‘hiccup’(35.1% → 41.2%) are especially improved. This indicatesthat focal loss can adapt to the sparsity of the label.
Next, we showed the results of the effect of the auxiliary pitch information in the middle part of Table 5.3.
A remarkable finding is that the F-measure of ‘falsetto’
was particularly improved (51.3% → 56.5%). We also
confirmed that the recall value of ‘falsetto’ is raised more
(64.7% → 74.1%) rather than the precision value (45.1%→ 48.3%). This indicates that pitch information hint at therelationship between the sung pitch value and the occurrence of falsetto (i.e., falsetto can appear only at a higher
pitch). We also confirmed that the input of CREPE pitch
shows the similar tendency.
We further investigated the effects of combining these
two types of auxiliary information. As shown in the bottom part of Table 5.3, the combination of GT pitch andfocal loss is the best condition in terms of both macro- andmicro-F values. We can confirm that both effects of each
auxiliary information occur under this condition (red and
5 The other metrics were following; 85.9% for voice recall, 29.9% for
voicing false alarms, 94.5% for raw pitch accuracy.

6. CONCLUSION AND FUTURE WORKThis work presented an analysis and identification of thesinging technique in J-POP music as a case study. In addition, we built a new dataset consisting of 168 J-POP
songs with annotation of pitch contour and singing techniques. The contributions of this study are summarized asfollows: 1) we constructed a dataset with frame-level annotations of singing techniques on 168 famous J-POP vocal
songs, 2) conducted a descriptive-statistical analysis of thedataset, 3) proposed a CRNN-based singing technique detection model as a baseline, and confirmed that both auxiliary pitch information and focal loss help the detection
performance.
As we showed the appearance of singing techniques
relates to some other factors (e.g., difference of singing
technique distribution by singer, pitch information helps
the detection of falsetto, etc.), we are planning further investigations of relationships or joint identification between
other musical factors (e.g. musical note, lyrics, beat andtempo, etc.) or higher level information (e.g. singer, songs’
emotion, mood, etc.) with making more types of annotation. Although this work focuses only on J-POP, theknowledge can be applicable to other types of singing.
Similar study on other style of vocal such as K-POP, western pops or non-popular vocal and comparison between
them are other interesting topics for future work.
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7. ACKNOWLEDGEMENTS
This work was supported by JST SPRING, Grant Number JPMJSP2124. Dr. Maiko Murai (Associate Prof. at
the University of Tsukuba) gave advices on copyright law.
Yoshihide Ishikawa, Sayori Takayama and Karolina Shi
helped the preparation of the metadata. We are gratefulto above all.
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