Introduction
Description
These are the results for the 2014 running of the Singing Voice Separation task set. The evaluation set is kindly provided by iKala. If you need to cite this page, please also cite T.-S. Chan, T.-C. Yeh, Z.-C. Fan, H.-W. Chen, L. Su, Y.-H. Yang, and R. Jang, "Vocal activity informed singing voice separation with the iKala dataset," in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process., 2015, pp. 718-722. For more information about this task set please refer to the 2014:Singing Voice Separation page.
Legend
| Submission code
|
Submission name
|
Abstract PDF
|
Contributors
|
| GW1
|
Bayesian Singing-Voice Separation |
PDF |
Guan-Xiang Wang, Po-Kai Yang, Chung-Chien Hsu, Jen-Tzung Chien
|
| HKHS1
|
Singing-Voice Separation using Deep Recurrent Neural Networks |
PDF |
Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis
|
| HKHS2
|
Singing-Voice Separation using Deep Recurrent Neural Networks |
PDF |
Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis
|
| HKHS3
|
Singing-Voice Separation using Deep Recurrent Neural Networks |
PDF |
Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis
|
| IIY1
|
Singing Voice Separation and Vocal F0 Estimation based on Robust PCA and Subharmonic Summation |
PDF |
Yukara Ikemiya, Katsutoshi Itoyama, Kazuyoshi Yoshii
|
| IIY2
|
Singing Voice Separation and Vocal F0 Estimation based on Robust PCA and Subharmonic Summation |
PDF |
Yukara Ikemiya, Katsutoshi Itoyama, Kazuyoshi Yoshii
|
| JL1
|
Singing Voice Separation Based on Sparse Nature and Spectral/Temporal Discontinuity |
PDF |
Il-Young Jeong, Kyogu Lee
|
| LFR1
|
Kernel Additive Modelling with light models |
PDF |
Antoine Liutkus, Derry Fitzgerald, Zafar Rafii
|
| RNA1
|
Singing Voice Separation using Adaptive Window Harmonic Sinusoidal Modeling |
PDF |
Preeti Rao, Nagesh Nayak, Sharath Adavanne
|
| RP1
|
REPET-SIM for Singing Voice Separation |
PDF |
Zafar Rafii, Bryan Pardo
|
| YC1
|
MIREX 2014 Submission for Singing Voice Separation |
PDF |
Frederick Yen, Tai-Shih Chi
|
Evaluation Criteria
GNSDR = Global Normalized Signal-to-Distortion Ratio
NSDR = Normalized Signal-to-Distortion Ratio
SIR = Signal-to-Interference Ratio
SAR = Signal-to-Artifacts Ratio
Summary
Summary Results
| Algorithm |
Voice GNSDR (dB) |
Music GNSDR (dB) |
Runtime (hh)
|
| GW1 |
2.8861 |
5.2549 |
24
|
| HKHS1 |
-1.3988 |
0.3483 |
06
|
| HKHS2 |
-1.9413 |
0.5239 |
06
|
| HKHS3 |
-2.4807 |
0.1414 |
06
|
| IIY1 |
4.2190 |
7.7893 |
02
|
| IIY2 |
4.4764 |
7.8661 |
02
|
| JL1 |
4.1564 |
5.6304 |
01
|
| LFR1 |
0.6499 |
3.0867 |
03
|
| RNA1 |
3.6915 |
7.3153 |
06
|
| RP1 |
2.8602 |
5.0306 |
01
|
| YC1 |
-0.8202 |
-3.1150 |
13
|
NSDR
For the Singing Voice (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GW1 |
2.8861 |
3.4543 |
-7.1344 |
12.819 |
2.5745 |
| HKHS1 |
-1.3988 |
3.0574 |
-9.4234 |
4.292 |
-1.2971 |
| HKHS2 |
-1.9413 |
3.2899 |
-11.309 |
7.2794 |
-1.4234 |
| HKHS3 |
-2.4807 |
3.8173 |
-12.272 |
9.7879 |
-1.5772 |
| IIY1 |
4.219 |
3.2378 |
-3.4536 |
15.517 |
4.4345 |
| IIY2 |
4.4764 |
3.0584 |
-2.3763 |
16.212 |
4.2927 |
| JL1 |
4.1564 |
3.9819 |
-3.9431 |
15.822 |
3.7558 |
| LFR1 |
0.64992 |
3.7455 |
-9.6199 |
7.4555 |
0.97393 |
| RNA1 |
3.6915 |
3.4319 |
-1.8064 |
14.38 |
3.4024 |
| RP1 |
2.8602 |
2.7926 |
-3.771 |
12.105 |
2.4553 |
| YC1 |
-0.82015 |
3.4857 |
-8.7424 |
7.9435 |
-0.42864 |
download these results as csv
For the Music Accompaniment (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GW1 |
5.2549 |
4.0553 |
-0.792 |
16.155 |
5.0222 |
| HKHS1 |
0.34825 |
2.207 |
-5.7359 |
6.1051 |
0.33855 |
| HKHS2 |
0.52394 |
2.5029 |
-6.1304 |
6.0994 |
0.90947 |
| HKHS3 |
0.14144 |
2.3196 |
-6.3693 |
5.8651 |
0.55883 |
| IIY1 |
7.7893 |
3.0938 |
-4.0068 |
13.949 |
8.1274 |
| IIY2 |
7.8661 |
3.5329 |
-2.4807 |
15.082 |
8.7023 |
| JL1 |
5.6304 |
4.0732 |
-0.91101 |
17.648 |
5.5284 |
| LFR1 |
3.0867 |
2.6421 |
-6.9241 |
10.887 |
2.9156 |
| RNA1 |
7.3153 |
2.9143 |
-5.9455 |
13.753 |
7.5214 |
| RP1 |
5.0306 |
3.004 |
-0.99542 |
15.424 |
4.9872 |
| YC1 |
-3.115 |
3.6797 |
-12.229 |
3.5503 |
-2.9997 |
download these results as csv
Boxplots
SIR
For the Singing Voice (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GW1 |
6.9844 |
9.43 |
-26.961 |
18.215 |
8.8768 |
| HKHS1 |
6.7499 |
10.673 |
-30.345 |
23.034 |
7.058 |
| HKHS2 |
8.3009 |
11.705 |
-32.287 |
29.393 |
7.8647 |
| HKHS3 |
7.7489 |
12.137 |
-30.839 |
28.544 |
8.9649 |
| IIY1 |
15.472 |
11.954 |
-28.445 |
32.446 |
18.307 |
| IIY2 |
13.267 |
11.466 |
-30.369 |
30.901 |
16.314 |
| JL1 |
9.6169 |
9.6173 |
-24.122 |
24.341 |
11.755 |
| LFR1 |
10.454 |
10.442 |
-26.952 |
23.638 |
13.042 |
| RNA1 |
16.323 |
10.951 |
-24.713 |
34.263 |
18.799 |
| RP1 |
7.2958 |
9.7631 |
-28.981 |
20.303 |
9.7841 |
| YC1 |
10.873 |
10.809 |
-28.646 |
27.301 |
12.837 |
download these results as csv
For the Music Accompaniment (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GW1 |
6.96 |
13.076 |
-12.643 |
42.653 |
4.3054 |
| HKHS1 |
1.4953 |
10.084 |
-13.232 |
38.909 |
-1.4525 |
| HKHS2 |
2.4162 |
10.465 |
-9.9978 |
34.081 |
-0.51852 |
| HKHS3 |
0.90212 |
9.7862 |
-11.02 |
34.345 |
-0.66779 |
| IIY1 |
12.44 |
8.1972 |
-0.61968 |
41.502 |
11.163 |
| IIY2 |
14.301 |
8.3307 |
0.49447 |
41.767 |
13.809 |
| JL1 |
5.6509 |
10.636 |
-13.16 |
39.5 |
4.3978 |
| LFR1 |
4.4493 |
10.109 |
-11.445 |
41.717 |
2.0394 |
| RNA1 |
12.938 |
8.5096 |
-1.3967 |
40.34 |
11.979 |
| RP1 |
5.5158 |
10.417 |
-11.092 |
44.235 |
4.6256 |
| YC1 |
0.90846 |
8.4936 |
-12.296 |
32.53 |
-0.63057 |
download these results as csv
Boxplots
SAR
For the Singing Voice (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GW1 |
10.398 |
6.6431 |
-13.219 |
19.227 |
11.757 |
| HKHS1 |
4.4392 |
5.1179 |
-16.316 |
17.676 |
5.1547 |
| HKHS2 |
3.6845 |
6.1018 |
-23.638 |
15.233 |
4.4692 |
| HKHS3 |
3.6391 |
5.615 |
-14.303 |
15.068 |
3.2243 |
| IIY1 |
7.7078 |
7.4547 |
-25.591 |
16.613 |
9.6827 |
| IIY2 |
8.5817 |
7.2202 |
-24.222 |
17.066 |
10.487 |
| JL1 |
10.026 |
7.5205 |
-16.962 |
21.028 |
11.47 |
| LFR1 |
4.729 |
5.6625 |
-23.426 |
12.721 |
5.5804 |
| RNA1 |
6.662 |
7.3118 |
-25.659 |
15.083 |
8.9188 |
| RP1 |
9.8241 |
6.5477 |
-13.189 |
24.156 |
11.033 |
| YC1 |
2.9058 |
5.0893 |
-21.403 |
8.872 |
4.0133 |
download these results as csv
For the Music Accompaniment (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GW1 |
8.7701 |
3.3088 |
-2.6918 |
16.204 |
8.6529 |
| HKHS1 |
4.4585 |
4.2757 |
-12.382 |
16.774 |
4.7823 |
| HKHS2 |
4.2321 |
4.22 |
-8.0849 |
14.269 |
4.5204 |
| HKHS3 |
5.3476 |
4.6397 |
-12.255 |
14.438 |
5.794 |
| IIY1 |
5.4262 |
3.1853 |
-2.678 |
15.981 |
5.2362 |
| IIY2 |
5.0379 |
3.325 |
-3.1753 |
16.62 |
5.0873 |
| JL1 |
9.6038 |
3.7963 |
-3.8019 |
17.577 |
10.158 |
| LFR1 |
4.8871 |
3.4349 |
-12.787 |
10.789 |
5.0422 |
| RNA1 |
4.7221 |
3.4545 |
-1.6892 |
15.052 |
4.9501 |
| RP1 |
7.6957 |
3.3901 |
-7.2754 |
14.854 |
7.9782 |
| YC1 |
-1.9525 |
2.8357 |
-12.203 |
6.2045 |
-2.3271 |
download these results as csv
Boxplots
Individual Spectrograms
As the MIREX test set is private, we use three other songs with similar characteristics to demonstrate the algorithms.
Labels
a = input mixture x
b = ground truth voice for x
c = extracted voice from x
d = input mixture y
e = ground truth voice for y
f = extracted voice from y
g = input mixture z
h = ground truth voice for z
i = extracted voice from z
Runtime Data
| Submission Code |
Runtime (hh) |
| GW1 |
24 |
| HKHS1 |
06 |
| HKHS2 |
06 |
| HKHS3 |
06 |
| IIY1 |
02 |
| IIY2 |
02 |
| JL1 |
01 |
| LFR1 |
03 |
| RNA1 |
06 |
| RP1 |
01 |
| YC1 |
13 |
download these results as csv
