Introduction
Description
These are the results for the 2016 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 2016:Singing Voice Separation page.
Legend
| Submission code
|
Submission name
|
Abstract PDF
|
Contributors
|
Training set
|
| GD1
|
Harmonic Modeling of Singing Voice for Source Separation |
PDF |
Georgi Dzhambazov |
Unknown
|
| HC1
|
MIREX 2016 Submission for Singing Voice Separation |
PDF |
Yi-Chun Huang, Tai-Shih Chi |
iKala
|
| LCP1
|
Deep Clustering for Singing Voice Separation |
PDF |
Yi Luo, Zhuo Chen, John R. Hershey, Jonathan Le Roux, Daniel P. W. Ellis |
SiSEC
|
| LCP2
|
Deep Clustering for Singing Voice Separation |
PDF |
Yi Luo, Zhuo Chen, John R. Hershey, Jonathan Le Roux, Daniel P. W. Ellis |
SiSEC
|
| MC2
|
MIREX 2016 Submission for Singing Voice Separation |
- |
Marius Miron, Pritish Chandna |
Unknown
|
| MC3
|
MIREX 2016 Submission for Singing Voice Separation |
- |
Marius Miron, Pritish Chandna |
Unknown
|
| RSGP1
|
Singing Voice Separation Using Deep Neural Networks and F0 Estimation |
PDF |
Gerard Roma, Emad M. Grais, Andrew J. R. Simpson, Mark D. Plumbley |
iKala
|
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 (m)
|
| GD1 |
-2.2810 |
0.3954 |
26.4413
|
| HC1 |
4.6309 |
7.8180 |
28.9727
|
| LCP1 |
6.0726 |
10.9256 |
37.8235
|
| LCP2 |
6.3414 |
11.1878 |
32.4800
|
| MC2 |
5.2891 |
9.6678 |
34.8084
|
| MC3 |
5.4920 |
9.8049 |
36.7194
|
| RSGP1 |
3.2589 |
8.7664 |
32.3578
|
NSDR
For the Singing Voice (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GD1 |
-2.281 |
3.534 |
-11.740 |
6.623 |
-1.935 |
| HC1 |
4.631 |
2.903 |
-1.127 |
14.260 |
3.883 |
| LCP1 |
6.073 |
3.462 |
-1.658 |
17.170 |
5.649 |
| LCP2 |
6.341 |
3.370 |
-1.958 |
17.240 |
5.997 |
| MC2 |
5.289 |
2.914 |
-1.302 |
12.571 |
4.945 |
| MC3 |
5.492 |
2.881 |
-0.453 |
12.448 |
5.195 |
| RSGP1 |
3.259 |
3.617 |
-14.027 |
12.045 |
3.304 |
download these results as csv
For the Music Accompaniment (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GD1 |
0.395 |
1.470 |
-2.260 |
5.825 |
0.211 |
| HC1 |
7.818 |
2.647 |
-1.258 |
15.861 |
8.046 |
| LCP1 |
10.926 |
3.835 |
0.742 |
19.960 |
10.883 |
| LCP2 |
11.188 |
3.626 |
2.508 |
19.875 |
11.087 |
| MC2 |
9.668 |
3.676 |
-7.875 |
22.734 |
9.926 |
| MC3 |
9.805 |
3.944 |
-7.679 |
22.453 |
10.099 |
| RSGP1 |
8.766 |
3.828 |
-6.905 |
18.065 |
8.966 |
download these results as csv
Boxplots
SIR
For the Singing Voice (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GD1 |
6.562 |
9.778 |
-30.324 |
24.205 |
9.043 |
| HC1 |
9.762 |
10.006 |
-28.081 |
23.070 |
12.713 |
| LCP1 |
13.822 |
10.320 |
-24.604 |
26.302 |
16.844 |
| LCP2 |
14.518 |
10.163 |
-23.923 |
26.606 |
17.467 |
| MC2 |
10.471 |
10.189 |
-29.559 |
28.854 |
12.621 |
| MC3 |
10.844 |
10.314 |
-28.896 |
29.313 |
13.346 |
| RSGP1 |
16.240 |
10.743 |
-26.850 |
33.092 |
18.471 |
download these results as csv
For the Music Accompaniment (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GD1 |
1.984 |
9.805 |
-11.864 |
37.146 |
0.840 |
| HC1 |
8.978 |
9.299 |
-2.271 |
42.987 |
7.576 |
| LCP1 |
24.015 |
7.915 |
5.929 |
47.039 |
23.596 |
| LCP2 |
25.170 |
7.220 |
11.146 |
48.129 |
24.578 |
| MC2 |
19.811 |
6.984 |
2.837 |
42.629 |
19.143 |
| MC3 |
19.609 |
7.091 |
2.366 |
43.881 |
18.602 |
| RSGP1 |
17.737 |
6.484 |
4.017 |
42.258 |
16.593 |
download these results as csv
Boxplots
SAR
For the Singing Voice (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GD1 |
2.394 |
4.562 |
-12.304 |
9.967 |
3.465 |
| HC1 |
10.768 |
7.287 |
-19.592 |
20.876 |
12.282 |
| LCP1 |
10.183 |
8.150 |
-23.437 |
20.348 |
12.538 |
| LCP2 |
10.137 |
8.305 |
-22.992 |
20.063 |
12.235 |
| MC2 |
11.243 |
7.441 |
-18.240 |
20.293 |
12.813 |
| MC3 |
11.248 |
7.498 |
-15.216 |
20.749 |
12.811 |
| RSGP1 |
6.627 |
7.140 |
-24.741 |
14.289 |
8.313 |
download these results as csv
For the Music Accompaniment (dB)
| Algorithm |
Mean |
SD |
Min |
Max |
Median |
| GD1 |
2.708 |
2.661 |
-6.024 |
10.608 |
2.948 |
| HC1 |
8.019 |
3.476 |
-3.232 |
16.401 |
7.483 |
| LCP1 |
7.168 |
3.732 |
-2.080 |
17.939 |
7.087 |
| LCP2 |
7.346 |
3.483 |
-0.713 |
18.004 |
7.451 |
| MC2 |
6.106 |
3.537 |
-2.182 |
13.294 |
6.145 |
| MC3 |
6.288 |
3.507 |
-3.431 |
13.943 |
6.559 |
| RSGP1 |
5.286 |
2.539 |
-1.849 |
13.933 |
5.373 |
download these results as csv
Boxplots
Runtime Data
| Submission Code |
Runtime (m) |
| GD1 |
26.4413 |
| HC1 |
28.9727 |
| LCP1 |
37.8235 |
| LCP2 |
32.4800 |
| MC2 |
34.8084 |
| MC3 |
36.7194 |
| RSGP1 |
32.3578 |
download these results as csv
