Interactive Deep Singing-Voice Separation Based on
Human-in-the-Loop Adaptation
Tomoyasu Nakano

Yuki Koyamat.nakano@aist.go.jp
National Institute of Advanced Industrial Science andTechnology (AIST), Tsukuba, Japan

koyama.y@aist.go.jp
National Institute of Advanced Industrial Science andTechnology (AIST), Tsukuba, Japan

Masahiro Hamasaki

Masataka Goto

masahiro.hamasaki@aist.go.jp
National Institute of Advanced Industrial Science andTechnology (AIST), Tsukuba, Japanm.goto@aist.go.jp
National Institute of Advanced Industrial Science andTechnology (AIST), Tsukuba, Japan

ABSTRACTThis paper presents a deep-learning-based interactive system separating the singing voice from input polyphonic music signals.
Although deep neural networks have been successful for singing
voice separation, no approach using them allows any user interaction for improving the separation quality. We present a frameworkthat allows a user to interactively fine-tune the deep neural modelat run time to adapt it to the target song. This is enabled by designing unified networks consisting of two U-Net architectures basedon frequency spectrogram representations: one for estimating thespectrogram mask that can be used to extract the singing-voice
spectrogram from the input polyphonic spectrogram; the other for
estimating the fundamental frequency (F0) of the singing voice.
Although it is not easy for the user to edit the mask, he or she
can iteratively correct errors in part of the visualized F0 trajectorythrough simple interaction. Our unified networks leverage the usercorrected F0 to improve the rest of the F0 trajectory through themodel adaptation, which results in better separation quality. We validated this approach in a simulation experiment showing that theF0 correction can improve the quality of singing-voice separation.
We also conducted a pilot user study with an expert musician, whoused our system to produce a high-quality singing-voice separation
result.

CCS CONCEPTS• Applied computing → Sound and music computing.

Input: Polyphonicmusic audio signals

Deep model(interactively updated)

Machine-estimated F0 traj.

Output: Separated
singing voice

User-corrected F0 traj.

User interaction (e.g., mouse dragging)
Human-in-the-loop model adaptation
Figure 1: Overview of interactive deep singing-voice separation, where the user can interact with the deep neural network model to fine-tune the quality of the separated singing
voice. The user feedback is provided as F0 trajectories, basedon which the model is adapted.
ACM Reference Format:
Tomoyasu Nakano, Yuki Koyama, Masahiro Hamasaki, and Masataka Goto.
2020. Interactive Deep Singing-Voice Separation Based on Human-in-theLoop Adaptation. In 25th International Conference on Intelligent User Interfaces (IUI ’20), March 17–20, 2020, Cagliari, Italy. ACM, New York, NY, USA,
5 pages. https://doi.org/10.1145/3377325.3377539

KEYWORDS
Singing-voice separation, deep learning, human-in-the-loop model
adaptation, F0 estimation

IUI ’20, March 17–20, 2020, Cagliari, Italy© 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM.
This is the author’s version of the work. It is posted here for your personal use. Not
for redistribution. The definitive Version of Record was published in 25th International
Conference on Intelligent User Interfaces (IUI ’20), March 17–20, 2020, Cagliari, Italy,
https://doi.org/10.1145/3377325.3377539.

1

INTRODUCTION

As the use of subscription music services has become commonplace,efficient music information retrieval systems and intelligent music
listening interfaces have become more and more important to increase the value of human music activities. Since singing voice is
one of the most important elements in music [17] and many listeners pay attention to the singing voice and its lyrics [6], singing-voice
separation from polyphonic music signals [30] has been a fundamental technology for various real-world applications. It can, forexample, be used to retrieve songs with singers having voice timbre
similar to that of a listener’s favorite artist [13], to improve theperformance of audio-to-lyrics synchronization for automaticallyIUI ’20, March 17–20, 2020, Cagliari, Italy

generating lyric videos [20], and to provide an intelligent musicplayback interface that allows the user to interactively change theplayback order of multiple songs according to vocal gender [26].
The majority of recently proposed singing-voice separation methods involve deep learning [11, 16, 19, 23, 24, 30, 32–34], where researchers have mainly focused on how to improve the accuracy of
singing-voice separation as much as possible for songs as various as
possible. Thus the main concern is to seek better training methods,
better model architectures, and larger training datasets that achieve
better accuracy with better generalization ability. It is, however,difficult for even state-of-the-art methods to achieve the perfect
separation (e.g., electric guitar sounds in some songs are sometimes
recognized as singing voice), and there is no means to improve
the quality except for feeding more training data, which is not afeasible option for end-users. Thus, with these methods it is noteasy to obtain a high-quality separation result for a specific song
even if a high interaction cost can be afforded.
In this work, we take a different approach: instead of using astatic general model for separation, we investigate fine-tuning of
an initial model based on U-Net architectures to adapt it to a specific target song to improve the separation accuracy for that song.
Specifically, we propose a human-in-the-loop model adaptation
framework (see Figure 1) where the user iteratively provides feedback to the model for fine-tuning for the target song. We call thisframework interactive deep singing-voice separation. The key idea
is to perform this feedback loop in the domain of fundamental
frequency (F0, which is a physical property of sound related topitch) of the singing voice, instead of the domain of separated audio
signals or their frequency spectrograms. This allows the user toeasily communicate with the model through simple interaction(e.g., mouse dragging) for correcting errors in the visualized F0
trajectory. In this framework, the user does not have to correct
all the F0 errors. After correcting some of them, the user can use
them to update the deep source separation model by adapting it to
the target song, which could be expected to result in automaticallycorrecting some remaining errors. This could reduce the burden of
manually correcting errors to achieve the perfect separation.
Note that our approach is complementary to the approach oftraining a general model; our framework could use any state-ofthe-art training methods, architectures, and datasets in the future
to train an initial model, and then let users interactively adapt themodel for a specific song to obtain even better separation.
Our contributions are summarized as follows:• We present the first singing-voice separation framework thatallows users to interactively fine-tune a deep-learning model
to a target song to increase separation accuracy.
• We propose to let users interact with estimated F0 trajectories instead of separated singing voices to provide feedback
to the model since it is much easier for humans to edit F0
trajectory than to edit audio signals.
• We validated through simulated experiments that our framework could improve the separation accuracy by feeding corrected F0 trajectories. We also validated through a pilot user
study that an expert musician could perform interactivemodel adaptation with our system and then obtain a better
singing-voice separation result.

Tomoyasu Nakano, Yuki Koyama, Masahiro Hamasaki, and Masataka Goto

2

RELATED WORK

User-guided sound source separation methods have already been
investigated, though they are not based on deep learning and do
not focus on singing voices. Those methods can leverage humaninteraction to improve the accuracy of sound source separation. For
example, by giving an audio example such as a singing or speaking
voice as side information, the corresponding sound source that issimilar to the example can be separated [25, 31]. The separation
accuracy can be improved by annotating the time when each soundsource exists alone [28], annotating the F0 by hand [9, 12], andannotating sound sources in the time-frequency domain [4, 7, 22].
These methods, however, have not shown how to use human interaction to improve the separation accuracy of deep learning. In
contrast, we investigate how users can be involved in the deeplearning-based separation process, and we show how to adapt the
deep model by using the F0 error correction.
Several methods that allow users to interactively fine-tune deepneural models have been proposed in domains other than singingvoice separation. For example, in the image processing domain,
researchers have proposed interactive image segmentation methods
that allow interactive fine-tuning [35, 36]. In this work, we present
the first interactive deep method for the singing-voice separation
problem and propose the novel idea of using the F0 trajectory as
the interface between the deep neural model and the user.
Our framework is related to the approach called interactive machine learning [1, 10] in that training of machine learning models
happens during user interaction. While the main purpose of interactive machine learning is to allow an end-user to rapidly train
a general model to the target problem, our framework uses interactivity for training a specific model that works well to a specificinstance of the target problem.

3

INTERACTIVE DEEP SINGING-VOICESEPARATION

We propose a deep-learning-based singing-voice separation methodthat utilizes user guides. Our key idea is to use the fundamental frequency (F0) of singing voice, which is an acoustic feature of singing
voice and is closely related to the improvement of singing-voice separation quality, as the interface for the user to interact with the deepmodel since it is much easier for users to correct F0 trajectories thanto correct separated singing voice signals. Performance improvements of F0 estimation and singing-voice separation in music havebeen used to improve each other’s performance [5, 8, 15]. Recently,
deep-learning-based methods that jointly estimate singing-voice
F0 and separated singing voice have also been proposed [18, 27].
Figure 2 shows our prototype system, which displays the input
polyphonic music audio signals, the estimated or user-corrected F0
trajectory, and the separated singing voice. The F0 estimation error
can be easily corrected by simple mouse-based interactions and isused as a user’s guide to update separated singing voice. The usercorrects F0 by dragging the mouse, uses the left button to draw
new F0 trajectory, and uses the right button to delete existing F0
trajectory. The parameters of the deep-learning model are tuned bypressing the “Update DNN” button after correcting the F0 trajectory.
Since our current implementation assumes that the user corrects

Interactive Deep Singing-Voice Separation Based on Human-in-the-Loop Adaptation

IUI ’20, March 17–20, 2020, Cagliari, Italy

Input

Estimated &user-corrected
F0 trajectories

SpectrogramF0 saliency map

Vocal mask
ConcatPlayhead

Spec.

U-Net

F0

U-Net

Vocal

Approach:

OutputConcatFigure 2: Screen capture of our prototype system. (Top) Input polyphonic music audio signals. (Middle) Widget for correcting the estimated F0 trajectory. (Bottom) Current outputof the separated-singing voice.
F0 from the beginning, the parameters are updated with the data
up to the point when the user last corrected it.
By tuning the model parameters, two effects can be obtained.
The first is to speed up the manual process of the F0 error correction.
Since the F0 of the uncorrected part can be re-estimated by the deeplearning model that is updated by using the partially corrected F0,
F0 error correction can be speeded up without correcting all F0
trajectories. Although such a function does not exist in the previous
work on interactive time-pitch annotation-informed separation [12],this kind of interaction was proposed for acoustic event detection[21]. The second effect obtained by tuning the model parameters is
to improve the singing-voice separation quality. The user can obtaina singing voice with improved separation quality by simply deleting
the F0 where the singing voice does not exist and, elsewhere, bydrawing the correct F0 of the singing voice part.

4 IMPLEMENTATION
4.1 Deep Singing-Voice Separation
We use the U-Net architectures for both the singing-voice separation and the F0 estimation. Based on Jansson et al. [19], amplitude
spectra obtained using the short-time Fourier transform (STFT)
are used for the input to the U-Net. They are computed with the
LibROSA python library from a monaural audio signal (with a 44.1
kHz sampling rate) that is obtained by averaging left and rightchannels in the original stereo audio signal of a song. The STFT
conditions and the U-Net architecture are the same as in [27] exceptthat the U-Net has only one decoder that outputs a vocal mask.
The output of the U-Net for the F0 estimation is represented as a
two-dimensional F0 saliency map (corresponding to the frequency
spectrogram) [2], in which the F0 saliency value is between 0 and 1
at each time-frequency bin. The ground truth of the F0 saliency
map is obtained as a Gaussian-blurred binary saliency spectrogram(i.e., quantized F0 trajectory) as proposed in [2].
In order to have the user’s F0 correction result contribute to theimprovement of the singing voice separation quality, we connect the
U-Net for the source separation and the U-Net for the F0 estimationand estimate their parameters jointly. There are various ways ofconnecting the U-Nets. Jansson et al. [18] reported that a joint

Spec.

U-Net

Vocal

U-Net

F0

Approach:
Figure 3: Overview of our model. Two U-Nets are concatenated to estimate both a vocal mask (SVS) and an F0 saliency
map (F0). We have two variants; the first one separates
singing voice after F0 estimation (F0 → SVS) and the second
one separates singing voice first (SVS → F0).
architecture repeating twice a network comprising the U-Net forthe source separation followed by the U-Net for the F0 estimation(i.e., four U-Nets in total after the repetition) achieved the best
performance. In order to reduce the computation time to improvethe user interactivity for our purpose, we implemented two typesof non-repetitive connection (Figure 3), one of which concatenates
the F0 estimation result to the U-Net input for the singing voice
separation (i.e., the U-Net for the F0 estimation followed by the UNet for the source separation) and the other of which concatenates
the singing-voice separation result to the U-Net input for the F0
estimation (i.e., the U-Net for the source separation followed by the
U-Net for the F0 estimation).
The whole connected network consisting of the two U-Nets
can be trained jointly by the back-propagation algorithm. Since
the user-corrected F0 can be used to compute the loss for the
back-propagation algorithm, the network parameters (i.e., the deepmodel) can be updated to adapt to the target song and its singing
voice. The connected network of the former type (F0 estimation
followed by singing-voice separation) directly updates the U-Net
parameters for the singing-voice separation by reflecting the usercorrected F0 and also re-estimates the unmodified F0. In contrast,
the connected network of the latter type (singing-voice separation
followed by F0 estimation) allows all the parameters of both of the
U-Nets to be updated by using the user-corrected F0.

4.2

Interaction

The F0 saliency map is first converted to the F0 trajectory because
it is hard for the user to directly correct the map. This conversion
is implemented by picking, at each time-frame, the maximum peakvalue in the F0 saliency map. If the peak value does not exceed thethreshold (0.1), the corresponding point on the map is consideredmute (silence). This method does not consider the case where there
are multiple simultaneous singing voices (multiple F0s at the sametime) since this paper focuses on a simple interaction to realize theproposed concept.

IUI ’20, March 17–20, 2020, Cagliari, Italy

Tomoyasu Nakano, Yuki Koyama, Masahiro Hamasaki, and Masataka Goto

6

5

EVALUATION

We conducted a simulation experiment assuming that the user gave
an ideal F0, and we conducted a pilot study with an expert musician.
To evaluate the singing-voice separation accuracy, the MedleyDB
dataset [3] and the RWC Music Database [14] were used. The dataconsisted of 150 songs, 50 from the former and 100 from the latter. From the MedleyDB dataset, we chose 50 songs that containsinging voices with a melody role and have F0 annotations. Fromneither the MedleyDB dataset nor the RWC Music Database did
we choose songs in which the vocal track contained other sounds
such as accompaniment piano sounds and the other tracks partiallycontained some singing voices due to the recording environment.

5.1

SDR (Vocal) [dB]

As shown in Figure 2, the beat timings and the frequencies corresponding to the note names (e.g., C3 and A3) are visualized as
grid lines in order to support correction by the user. Beat timing
was automatically estimated using the madmom python library.

Simulation Experiment

To evaluate the separation accuracy by 5-fold cross validation, the150 songs were randomly divided into five 30-song groups. The
standard evaluation tool mir_eval [29] was used for computing thesignal-to-distortion ratio (SDR) for the singing-voice separation.
The SDR was calculated for each song, and then its median over all
songs was calculated.
By using the training set of the cross validation, the model parameters were first trained for 256 epochs (iterations). Then, tosimulate a situation where a user partially or fully corrected F0
trajectories, we partially or fully used the correct F0 annotationof each song in the training set to fine-tune the parameters for 32
epochs. From the beginning of each song, we used 25%, 50%, 75%,
or 100% of the annotation.
We compared the performances of the following conditions:• SVS (0%): A single U-Net for the singing-voice separation(without using the F0 estimation).
• SVS → F0 (0%): The U-Net for the singing-voice separation
followed by the U-Net for the F0 estimation1 .
• F0 (0%) → SVS: The U-Net for the F0 estimation followedby the U-Net for the singing-voice separation.
• SVS → F0 (25–100%) or F0 (25–100%) → SVS: Our deep
singing-voice separation with the model adaptation.
Note that the percentage value represents the ratio of the correct
F0 annotation used for the adaptation (fine-tuning). 0% means thatnone of the F0 annotation is used and 100% means that all the F0
annotation of each song is used.
Figure 4 shows the results. The network that separates singing
voice after F0 estimation (F0 → SVS) had lower separation quality
than the network that separates singing voice first (SVS → F0)
when no model adaptation was performed (0%), but it had higher
separation quality when a sufficient amount (50%, 75%, and 100%) of
the correct F0 annotation (i.e., the user’s F0 correction) was given. In
addition, even when the F0 correction was performed only partially(F0 (50%) → SVS), the result showed higher performance than thecase without the F0 correction. Furthermore, our method with the
1 Note that our connection method is different from that used in previous research [18].

SVS
SVS → F0F0 → SVS

5

4.52

4.73

5.04

4.03

4

4.03

3.67
3.75

4.28
3.48

3

3.83

2.74
2
(N/A)

0%

25%

50%

75%

100%

Data ratio used for model adaptation

Figure 4: SDR value (higher is better) in each condition.

sufficient model adaption, F0 (100%) → SVS, greatly outperformed
the methods without the model adaptation (0%).

5.2

Pilot User Study

We also conducted an informal pilot study to see whether our
main target users (i.e., expert musicians who are familiar with theconcept of F0) could correct estimated F0 trajectories and fine-tune
the model to obtain better results. The participant was an expert
musician who had experience in correcting estimated F0 trajectories
of singing voice using existing software, Audionamix TRAX PRO
3. We asked the participant to separate one song, RWC-MDB-P
No. 007 [14]. We asked the participant to first check the initial F0estimation and singing-voice separation results and then begin theiteration of F0 correction and model adaptation for 30 minutes.
In an informal interview after this session, the participant commented that the fine-tuning to automatically improve the rest of thearea by model adaptation was effective. For example, we observedthat errors such as a guitar solo mistakenly separated as singing
voice were automatically corrected by model adaptation with F0
correction of other parts.

6

CONCLUSION

This paper presented an interactive deep-learning-based frameworkthat can separate the singing voice from input polyphonic musicsignals. The key idea was to involve the user in the loop to obtain
feedback about the target song, based on which the deep model canbe fine-tuned. The simulation experiment showed that our framework could improve singing-voice separation quality. In addition,
we have shown in a user study that a user successfully corrected
F0 trajectories to improve singing-voice separation quality.
The current interaction for F0 correction is a simple mouse operation, and a future challenge is to improve usability with intelligent
support (e.g., like that provided by a magnetic selection tool used inimage-editing software and some commercial singing-voice separation software). We also plan to investigate the use of information
other than F0 for even better human-in-the-loop singing-voice separation.

ACKNOWLEDGMENTS
This work was supported in part by JST ACCEL (JPMJAC1602).

Interactive Deep Singing-Voice Separation Based on Human-in-the-Loop Adaptation

REFERENCES[1] S. Amershi, M. Cakmak, W. B. Knox, and T. Kulesza. 2014. Power to the People:
The Role of Humans in Interactive Machine Learning. AI Magazine 35, 4 (Dec.
2014), 105–120.
[2] R. Bittner, B. McFee, J. Salamon, P. Li, and J. P. Bello. 2017. Deep Salience
Representations for F0 Estimation in Polyphonic Music. In ISMIR 2017. 63–70.
[3] R. M. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam, and J. P. Bello. 2014.
MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research. In
ISMIR 2014. 155–160.
[4] J. Bryan, Gautham J. Mysore, and Ge Wang. 2014. ISSE: An Interactive Source
Separation Editor. In ACM CHI 2014.
[5] P. Cabañas-Molero, D. Martínez Muñoz, M. Cobos, and J. J. López. 2011. Singing
Voice Separation from Stereo Recordings using Spatial Clues and Robust F0
Estimation. In AEC Conference.
[6] A. Demetriou, A. Jansson, A. Kumar, and R. M. Bittner. 2018. Vocals in Music
Matter: the Relevance of Vocals in the Minds of Listeners. In ISMIR 2018. 514–520.
[7] N. Q. K. Duong, A. Ozerov, L. Chevallier, and J. Sirot. 2014. An Interactive Audio
Source Separation Framework based on Non-negative Matrix Factorization. In
ICASSP 2014.
[8] J. Durrieu, B. David, and G. Richard. 2011. A Musically Motivated Mid-Level
Representation for Pitch Estimation and Musical Audio Source Separation. IEEE
J. Sel. Topics Signal Process. 5, 6 (2011), 1180–1191.
[9] J.-L. Durrieu and J.-P. Thiran. 2012. Musical Audio Source Separation based on
User-selected F0 Track. In LVA/ICA 2012. 438–445.
[10] J. A. Fails and Jr. D. R. Olsen. 2003. Interactive Machine Learning. In ACM IUI
2003.
[11] Z. C. Fan, J. S. R. Jang, and C. L. Lu. 2016. Singing Voice Separation and Pitch
Extraction from Monaural Polyphonic Audio Music via DNN and Adaptive Pitch
Tracking. In BigMM 2016. 178–185.
[12] B. Fuentes, R. Badeau, and G. Richard. 2012. Blind Harmonic Adaptive Decomposition Applied to Supervised Source Separation. In EUSIPCO 2012.
[13] H. Fujihara, M. Goto, T. Kitahara, and H. G. Okuno. 2010. A Modeling of Singing
Voice Robust to Accompaniment Sounds and its Application to Singer Identification and Vocal-timbre-similarity-based Music Information Retrieval. IEEE Trans.
on Audio, Speech, and Language Processing 18, 3 (2010), 638–648.
[14] M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka. 2002. RWC Music Database:
Popular, Classical, and Jazz Music Databases. In ISMIR 2002. 287–288.
[15] C. L. Hsu, D. Wang, J. R. Jang, and K. Hu. 2012. A Tandem Algorithm for Singing
Pitch Extraction and Voice Separation From Music Accompaniment. IEEE Trans.
Acoust., Speech, Signal Process. 20, 5 (2012), 1482–1491.
[16] P. S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis. 2014. Singing-voice
Separation from Monaural Recordings using Deep Recurrent Neural Networks.
In ISMIR 2014. 477–482.
[17] E. J. Humphrey, S. Reddy, P. Seetharaman, A. Kumar, R. M. Bittner, A. Demetriou,
S. Gulati, A. Jansson, T. Jehan, B. Lehner, A. Krupse, and L. Yang. 2019. An
Introduction to Signal Processing for Singing-Voice Analysis: High Notes in the
Effort to Automate the Understanding of Vocals in Music. IEEE Signal Processing
Magazine 36, 1 (2019), 82–94.
[18] A. Jansson, R. M. Bittner, S. Ewert, and T. Weyde. 2019. Joint Singing Voice
Separation and F0 Estimation with Deep U-Net Architectures. In EUSIPCO 2019.
[19] A. Jansson, E. J. Humphrey, N. Montecchio, R. Bittner, A. Kumar, and T. Weyde.
2017. Singing Voice Separation with Deep U-Net Convolutional Networks. In
ISMIR 2017. 745–751.
[20] J. Kato, T. Nakano, and M. Goto. 2015. TextAlive: Integrated Design Environmentfor Kinetic Typography. In ACM CHI 2015.
[21] B. Kim and B. Pardo. 2018. A Human-in-the-Loop System for Sound Event
Detection and Annotation. TiiS 8, 2 (2018), 13:1–13:23.
[22] A. Lefevre, F. Bach, and C. Fevotte. 2012. Semi-supervised NMF with Timefrequency Annotations for Single-channel Source Separation. In ISMIR 2012.
[23] K. W. E. Lin, B. T. Balamurali, E. K., S. L., and D. Herremans. 2018. Singing Voice
Separation using a Deep Convolutional Neural Network Trained by Ideal Binary
Mask and Cross Entropy. Neural Computing and Applications (2018), 1–14.
[24] Y. Luo, Z. Chen, J. R. Hershey, J. Le Roux, and N. Mesgarani. 2017. Deep Clusteringand Conventional Networks for Music Separation: Stronger Together. In ICASSP
2017. 61–65.
[25] L. Le Magoarou, A. Ozerov, and N. Duong. 2013. Text-Informed Audio Source
Separation using Nonnegative Matrix Partial Co-Factorization. In MLSP 2013.
[26] T. Nakano, J. Kato, M. Hamasaki, and M. Goto. 2016. PlaylistPlayer: An Interface
Using Multiple Criteria to Change the Playback Order of a Music Playlist. In
ACM IUI 2016. 186–190.
[27] T. Nakano, K. Yoshii, Y. Wu, R. Nishikimi, K. W. E. Lin, and M. Goto. 2019. JointSinging Pitch Estimation and Voice Separation Based on a Neural Harmonic
Structure Renderer. In IEEE WASPAA 2019.
[28] A. Ozerov, C. Fevotte, R. Blouet, and J.-L. Durrieu. 2011. Multichannel Nonnegative Tensor Factorization with Structured Constraints for User-guided Audio
Source Separation. In ICASSP 2011.

IUI ’20, March 17–20, 2020, Cagliari, Italy[29] C. Raffel, B. McFee, E. J. Humphrey, J. Salamon, O. Nieto, D. Liang, , and D. P. W.
Ellis. 2014. mir_eval: A Transparent Implementation of Common MIR Metrics.
In ISMIR 2014. 367–372.
[30] Z. Rafii, A. Liutkus, F-R Stoter, S.I. Mimilakis, D. FitzGerald, and B. Pardo. 2018.
An Overview of Lead and Accompaniment Separation in Music. IEEE/ACM
Transactions on Audio, Speech, Language Processing 26, 8 (2018), 1307–1335.
[31] P. Smaragdis and G. Mysore. 2009. “Separation by Humming”: User Guided
Sound Extraction from Monophonic Mixtures. In WASPAA 2009.
[32] D. Stoller, S. Ewert, and S. Dixon. 2017. Wave-U-Net: A Multi-scale Neural
Network for End-to-end Audio Source Separation. In ISMIR 2017. 330–340.
[33] D. Stoller, S. Ewert, and S. Dixon. 2018. Jointly Detecting and Separating Singing
Voice: A Multi-Task Approach. In LVA/ICA 2018. 329–339.
[34] N. Takahashi and Y. Mitsufuji. 2017. Multi-scale Multi-band DenseNets for Audio
Source Separation. In WASPAA 2017. 21–25.
[35] G. Wang, W. Li, M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Doel, A. L.
David, J. Deprest, S. Ourselin, and T. Vercauteren. 2018. Interactive Medical Image
Segmentation Using Deep Learning With Image-Specific Fine Tuning. IEEE Trans.
on Medical Imaging 32, 7 (2018), 1562–1573.
[36] N. Xu, B. L. Price, S. Cohen, J. Yang, and T. S. Huang. 2016. Deep Interactive
Object Selection. In CVPR 2016.