SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Figure 2: Loss surface of  $\mathcal{L}_{\mathrm{MC}}$  in (4) with  $P = 6$  and  $C = 2$  (i.e., over-determined conditions) against hypothesized separation outputs generated by using various  $\mu$  and  $\nu$ . Best viewed in color.
C Effectiveness of FCP at approximating speaker images
Following the derivation in [29], let us define the mixture as  $Y_{p} = X_{p}(c) + V_{p}(c)$ , where  $V_{p}(c)$  consists of the signals of all the sources but  $c$ . We can formulate (6) as
$$
\begin{array}{l} \underset {\mathbf {g} _ {p} (c, f)} {\operatorname {a r g m i n}} \sum_ {t} \frac {1}{\hat {\lambda} _ {p} (c , t , f)} \left| X _ {p} (c, t, f) + V _ {p} (c, t, f) - \mathbf {g} _ {p} (c, f) ^ {\mathsf {H}} \widetilde {\tilde {\mathbf {Z}}} (c, t, f) \right| ^ {2} \\ \approx \underset {\mathbf {g} _ {p} (c, f)} {\operatorname {a r g m i n}} \sum_ {t} \frac {1}{\hat {\lambda} _ {p} (c , t , f)} \left| X _ {p} (c, t, f) - \mathbf {g} _ {p} (c, f) ^ {\mathsf {H}} \widetilde {\tilde {\mathbf {Z}}} (c, t, f) | ^ {2} + | V _ {p} (c, t, f) \right| ^ {2} \\ = \underset {\mathbf {g} _ {p} (c, f)} {\operatorname {a r g m i n}} \sum_ {t} \frac {1}{\hat {\lambda} _ {p} (c , t , f)} \left| X _ {p} (c, t, f) - \mathbf {g} _ {p} (c, f) ^ {\mathrm {H}} \tilde {\hat {\mathbf {Z}}} (c, t, f) \right| ^ {2}, \tag {15} \\ \end{array}
$$
where the derivation from the first row to the second is based on the assumption that, as the DNN training continues,  $\hat{Z}(c)$  would become more and more accurate so that, after some epochs, it can become uncorrelated (or little correlated) with  $V_p(c)$ , meaning that the cross-term would be small:
$$
\sum_ {t} \frac {1}{\hat {\lambda} _ {p} (c , t , f)} \left(X _ {p} (c, t, f) - \mathbf {g} _ {p} (c, f) ^ {\mathrm {H}} \widetilde {\mathbf {Z}} (c, t, f)\right) ^ {\mathrm {H}} V _ {p} (c, t, f) \approx 0. \tag {16}
$$
From the third row of (15), the resulting  $\hat{\mathbf{g}}_p(c,f)^\mathsf{H}\widetilde{\hat{\mathbf{Z}}} (c,t,f)$  would approximate  $X_{p}(c,t,f)$ .
D Illustration of frequency permutation problem
We use three-channel input and loss for DNN training. Fig. 3(a) and (b) show an example separation result of the model trained with  $\mathcal{L}_{\mathrm{MC}}$  in (9). Comparing the separated speech in Fig. 3(a) and (b) with the clean speech in (c) and (d), we can see that the separated speech suffers from the frequency permutation problem approximately in the range [1.6, 2.9] kHz. Fig. 3(e) and (f) show the separation result of the model trained with  $\mathcal{L}_{\mathrm{MC + ISMS}}$  in (11), which effectively addresses the frequency permutation problem.
(e)
Time (s)
Figure 3: Example spectrograms of (a)-(b): FCP-estimated speaker image 1 and 2 with SDR scores of 8.7 and 7.7 dB (using  $\mathcal{L}_{\mathrm{MC}}$  in (9) for training); (c)-(d): oracle speaker image 1 and 2; and (e)-(f): FCP-estimated speaker image 1 and 2 with SDR scores of 17.1 and 16.8 dB (using  $\mathcal{L}_{\mathrm{MC + ISMS}}$  in (11) for training). The blue rectangles in (a) and (b) mark the region with frequency permutation. The mixture SDR scores of the two speakers are respectively 0.2 and  $-0.1$  dB. Best viewed in color.
(f)
Time (s)
E Differences between mixture-constraint loss and mixture consistency
The proposed mixture-constraint loss,  $\mathcal{L}_{\mathrm{MC}}$ , has very different physical meanings and mathematical forms compared to the mixture consistency term proposed in [59]. First, in [59], DNN estimates are strictly constrained to add up to the mixture (see Eq. (7) and (9) in [59]), while our MC loss only encourages the filtered DNN estimates to add up to the mixture. Second, our MC loss is applied to filtered DNN estimates rather than directly to DNN estimates. Third, we deal with multi-microphone MC, while [59] only addresses single-channel cases. Fourth, the motivation of the proposed MC loss is to use mixtures as constraints to regularize DNN estimates so that the estimates can approximate source images. This motivation is completely different from that of the mixture consistency term.
F Illustration of using causal and non-causal filtering
This section illustrates that the relative RIR relating a signal to its delayed version is causal and the relative RIR relating a signal to its advanced version is non-causal.
In Fig. 4, suppose that the blue signal is the DNN estimate for speaker  $c$ , and the orange signal is speaker  $c$ 's image at another microphone, which is a delayed version (i.e., reaching the microphone later). To filter the blue signal to approximate the oracle one, we only need a causal filter to delay the blue signal.
Reversely, suppose that the orange signal is the DNN estimate for speaker  $c$ , and the blue signal is speaker  $c$ 's image at another microphone, which is an advanced version (i.e., reaching the microphone earlier). To filter the orange signal to approximate the blue signal, we need a non-causal filter to advance the orange signal.
Figure 4: Example for illustrating causal and non-causal filtering. Best viewed in color.
G Interpretation of intermediate DNN estimates  $\hat{Z}$
This section provides an interpretation of the physical meanings of the intermediate DNN estimates  $\hat{Z}$  in the cases of using multi-channel input and loss, and single-channel input and multi-channel loss.
G.1 Multi-channel-input case
In (9),  $\hat{Z}(c)$  is constrained such that it can be filtered by a causal filter  $\hat{\mathbf{g}}_p(c)$  to approximate  $X_p(c)$ . Since there could be an infinite number of combinations of  $\hat{Z}(c)$  and  $\hat{\mathbf{g}}_p(c)$  whose convolution results would well approximate  $X_p(c)$ ,  $\hat{Z}(c)$  cannot be interpreted as the dry source signal and we think it more similar to a signal captured by a virtual microphone that is closer to speaker  $c$  than all the  $P$  microphones. See Fig. 5(a) for an example, where each virtual microphone captures the direct-path signal of a target speaker earlier than any other microphones so that we can use causal FCP filters.
G.2 Monaural-input case
In the monaural-input case, each  $\hat{Z}(c)$  would be aligned to the speaker's image at the input microphone (i.e., the reference microphone), since the DNN only has monaural input and in this case the DNN is not likely to align its outputs to a virtual microphone or any actual microphones.
We give an example in Figure 5(b), where the reference microphone captures speaker 2's direct-path signal later than all the other microphones. In this case, we just need to use non-causal FCP filters when filtering  $\hat{Z}(c)$  (which is estimated based on the monaural signal at the reference microphone) to approximate speaker 2's images captured at the other microphones.
(b)
Figure 5: Illustration of signal alignment in (a) multi-channel-input, multi-channel-loss case; and (b) single-channel-input, multi-channel-loss case. Best viewed in color.
H Differences from RAS
In many aspects, UNSSOR differs from RAS [36], which deals with monaural two-speaker separation given binaural (two-channel) training mixtures. RAS first performs DNN-based monaural separation on the left-ear mixture in the magnitude T-F domain, then linearly filters the DNN estimates at the left ear through time-domain Wiener filtering such that the filtered estimate can approximate the right-ear mixture, and the training loss is computed between the summation of the filtered DNN estimates and the right-ear mixture. Besides differences in, for example, the DNN architectures, linear filtering algorithms, how phase estimation (important for estimating relative RIRs) is handled, how frequency permutation problem is dealt with, and training data curation (difficult training examples need to be removed to train RAS), the key difference is that RAS fails to be trained in an unsupervised way [36]. It still needs labelled mixtures so that a supervised PIT-based model can be trained and then used as the starting point for their unsupervised algorithm.
We think that the ineffectiveness of RAS in fully-unsupervised setup is likely because the loss is computed only on the right-ear mixture. Following our analysis in Section 3, in RAS there are  $N \times 1$  equations (because the loss is only computed on the right-ear time-domain signal, assumed  $N$ -sample) but  $N \times C + (2 - 1) \times 512 \times C$  unknowns (where the 512 term is because the filter is assumed 512-tap in [36], and the  $(2 - 1)$  term is because there is only one filter for each speaker in the binaural setup, i.e., only one non-reference microphone). This is an ill-posed problem, not likely to be solved via the current RAS algorithm.
I Miscellaneous system and DNN setup
In default, for STFT, the window size is  $32\mathrm{ms}$ , the hop size is  $8\mathrm{ms}$ , and the square-root Hann window is used as the analysis window. For  $8\mathrm{kHz}$  sampling rate, a 256-point discrete Fourier transform is applied to extract 129-dimensional complex STFT spectra at each frame. This STFT setup is very common in modern deep learning based separation. Differently, the window and hop sizes of some baselines such as IVA and spatial clustering are considerably larger (see detailed setup in Section 5.1), as we observe that this leads to better separation performance, likely because more reverberation can be covered in each frame and, this way, their model assumptions can be better satisfied.
Our DNN architecture is TF-GridNet [23]. Using the symbols defined in Table I of [23], we set its hyper-parameters to  $D = 48$ ,  $B = 4$ ,  $I = 4$ ,  $J = 1$ ,  $H = 192$ ,  $L = 4$  and  $E = 4$  for  $8\mathrm{kHz}$  sampling rate. Please do not confuse the symbols in TF-GridNet with the ones in this paper.
In each epoch, we sample an  $l$ -second mixture segment from each training mixture for model training. If a mixture is shorter than  $l$  seconds, we pad zeros in the front rather than in the end, since padding in the end would result in a mixture that has abrupt stop of reverberation, which is not realistic and would be detrimental to FTP-based relative RIR estimation. In comparison, padding zeros in the front can avoid this problem. If a mixture is longer than  $l$  seconds, we randomly pick an  $l$ -second segment. In default,  $l$  is set to four seconds.
We normalize the sample variance of each sampled mixture segment to 1.0, before feeding them to DNN for training. Adam (with the default setup in Pytorch v1.9) is used as the optimizer. The
Table 5: Supplementary averaged results of 2-speaker separation on SMS-WSJ (6-channel input and loss).
Row	Systems	I	J	Loss	Val. set SDR (dB)	Test set
SDR (dB)	SI-SDR (dB)	PESQ	eSTOI
2a	UNSSOR	19	0	LCMC+ISMS	16.0	15.6	14.6	3.44	0.885
3e	iRAS w/ non-causal 1472-tap filters (64 future taps)	-	-	LCiRAS	2.4	2.4	0.2	1.89	0.549
Table 6: Supplementary averaged results of 2-speaker separation on SMS-WSJ (3-channel input and loss).
Row	Systems	I	J	Loss	Val. set SDR (dB)	SDR (dB)	Test set
SI-SDR (dB)	PESQ	eSTOI
2a	UNSSOR	19	0	LCMC+ISMS	15.7	15.4	14.4	3.20	0.874
3e	iRAS w/ non-causal 1472-tap filters (64 future taps)	-	-	LCiRAS	7.7	7.2	5.4	1.87	0.621
Table 7: Supplementary averaged results of 2-speaker separation on SMS-WSJ (1-ch input and 6-ch loss).
Row	Systems	I	J	Loss	Val. set SDR (dB)	Test set
SDR (dB)	SI-SDR (dB)	PESQ	eSTOI
1a	UNSSOR	19	1	LCMC+HSMS	13.0	12.5	11.9	3.27	0.832
2c	iRAS w/ non-causal 1536-tap filters (64 future taps)	-	-	LCiRAS	1.7	1.7	0.0	1.90	0.561
Table 8: Supplementary averaged results of 2-speaker separation on SMS-WSJ (1-ch input and 3-ch loss).
Row	Systems	I	J	Loss	Val. set SDR (dB)	Test set
SDR (dB)	SI-SDR (dB)	PESQ	eSTOI
1a	UNSSOR	19	1	LCMC+ISMS	12.5	12.0	11.4	3.18	0.822
2c	iRAS w/ non-causal 1536-tap filters (64 future taps)	-	-	LCiRAS	1.3	1.3	-0.3	1.87	0.549
$L_{2}$  norm for gradient clipping is set to 1.0. The learning rate starts from  $10^{-3}$  and is halved if the validation loss is not improved in two epochs. We terminate training once the learning rate is reduced to  $6.25 \times 10^{-5}$ . The batch size is set to four, with each segment being 4-second long. For each model, an Nvidia A100 40GB GPU is used for training, and the model converges in three to four days.
We sweep  $\gamma$  in (11) based on the set of  $\{0.02, 0.04, 0.06, 0.1, 0.3, 1.0\}$ . The microphone weight  $\alpha_{p}$  in (4), (9), (10) and (12) is set to 1.0 in default for all microphones (i.e., no weighting), and, in the monaural input case (e.g., in Table 3 and 4), we sweep  $\alpha_{1}$  at the reference microphone 1 based on the set of  $\{1/2, 1/3, 1/4, 1/5, 1/6, 1/8, 1/10\}$  to alleviate overfitting to microphone 1.
J Alternative filter length for iRAS and UNSSOR
We also provide the results of iRAS that uses the same filter length (in seconds) as that in UNSSOR. Given  $8\mathrm{kHz}$  sampling rate, 32 ms window size, 8 ms hop size, and  $K = I + 1 + J$  (defined below (6)) filter taps in FCP, we use  $M = ((K - 1)\times 8 + 32) / 1000\times 8000$  filter taps for each  $\hat{h}_p(c)$  in (12), and configure  $\hat{h}_p(c)$  to filter the past  $M - 8 / 1000\times 8000 - 1$ , the current, and the future 64  $(= 8 / 1000\times 8000)$  samples. We filter the future  $8\mathrm{ms}$  of samples, because, in the STFT case, the hop size is  $8\mathrm{ms}$ . We report the results in Table 5-8, each respectively corresponding to the results in Table 1-4. We observe that the performance is not as good as that of UNSSOR.
In Fig. 6, we make further comparisons of using different filter taps between UNSSOR and iRAS, following the experimental setup in the previous paragraph. Fig. 6(a) uses six-microphone input, Fig. 6(b) uses monaural input, and both of them use the six-microphone  $\mathcal{L}_{\mathrm{MC + ISMS}}$  (or  $\mathcal{L}_{\mathrm{iRAS}}$ ) loss. For UNSSOR, in Fig. 6(a) we set  $J = 0$  and sweep  $K \in \{5,9,13,17,20,25\}$  and in Fig. 6(b) we set  $J = 1$  and sweep  $K \in \{5,9,13,17,21,25\}$ . For iRAS, we configure the filter to always filter the future 64 samples, and in Fig. 6(a) we sweep the filter taps  $M \in \{128,256,384,512,768,1024,1280,1472\}$  and in Fig. 6(b) we sweep  $M \in \{128,256,384,512,768,1024,1280,1536\}$ . Notice that in Fig. 6, the filter taps  $M$  in iRAS and  $K$  in UNSSOR are vertically corresponding to each other. That is, some swept filter taps  $M$  are computed based on the swepted  $K$  (e.g., for  $M = 1024$  and  $K = 13$ , we have  $1024 = ((13 - 1) \times 8 + 32) / 1000 \times 8000$ ) so that they can result in the same filter length in time. From the figures, we observe that the best filter length (in seconds) is different for UNSSOR and iRAS, and the best performance of UNSSOR is higher than that of iRAS.
Figure 6: Averaged SDR (dB) results of UNSSOR and iRAS using various filter taps in cases of using (a) 6-channel input and loss; and (b) 1-channel input and 6-channel loss on SMS-WSJ. Vertically-corresponded  $M$  and  $K$  mean that the filter lengths in time are the same. Best viewed in color.
We point out that using a very long filter (i.e., a large  $M$ ) for time-domain Wiener filtering would prevent separation, since, in that case, the filter would have a large degrees of freedom to filter  $\hat{z}(c)$  to fit  $y_{p}$  very well and obtain a small  $\mathcal{L}_{\mathrm{iRAS}}$  (see (13)), even though  $\hat{z}(c)$  is not a good separation result. From Fig. 6(a), it can be observed that a very large  $M$  (e.g., 1472) does not yield good separation; and in addition, a very small  $M$  (e.g., 128) is also not good, as the linear filter could be just too short to fit the mixture  $y_{p}$  well. Similar trend can also be observed in the results of UNSSOR. For example, in Fig. 6(a), setting  $K$  to 5 and 25 produces worse performance than to 20. We can see that the filter length is an important hyper-parameter to tune.
We emphasize that, to compute the closed-form solution of time-domain Wiener filtering (see (13)), we need to invert a big  $M \times M$  matrix for each mixture, while in FCP (see (6)), we only need to invert a much smaller  $K \times K$  matrix for each of the  $F$  frequencies. Using FCP is clearly less computationally-expensive, given that the time complexity of matrix inversion is typically  $\mathcal{O}(n^3)$ . This also indicates that if the same amount of computation is required for linear filtering, FCP can use a much longer filter (in time) than time-domain Wiener filtering.
K UNSSOR's effectiveness when used with other DNN architectures
We observe that the effectiveness of the proposed system comes from both the strong modelling capabilities of TF-GridNet and the contributions of the UNSSOR mechanism itself. Without using UNSSOR to deal with the ill-posed problem, the modelling capability of strong DNNs cannot be unleashed to separate speakers; and without using a strong DNN, the patterns in speech cannot be modelled well to realize good separation.
We expect UNSSOR to work with many DNN architectures, as long as the architecture is reasonably strong and can effectively deal with reverberation. To validate this, we experiment UNSSOR with DNN arachitectures with lower modelling capability. We select two representative separation models from the literature, TCN-DenseUNet [74] and DPRNN [75].
We replace TF-GridNet with the TCN-DenseUNet described in [74], and train the network using the same training configurations. TCN-DenseUNet [74] contains a temporal convolution network sandwiched by a UNet with DenseNet blocks. It is a reasonably strong separation model, which is fully convolutional and shares many similarities with many contemporary DNN architectures [79-83]. According to [23], it is worse than TF-GridNet in supervised separation tasks. We provide the unsupervised separation results in Table 9, which are obtained by using six-channel input and loss. We observe that UNSSOR works to some extent with TCN-DenseUNet, and the results are not as good as the ones in Table 1 obtained by using TF-GridNet.
The DPRNN architecture [75] in our experiments has a window size of  $4\mathrm{ms}$  and a hop size of  $1\mathrm{ms}$ . It has 6 layers. The number of bases is 256. The bottleneck dimension is 128. The number of hidden units in each BLSTM in each direction is 128. We apply ReLU as the encoder non-linearity and as the non-linearity for embedding masking. The chunk size is set to 64 and the chunk overlap is  $50\%$ . To leverage spatial information for model training, we follow the strategy proposed in [84] (see its Fig. 2 to get the idea), where spectral embeddings are learned together with spatial embeddings and
Table 9: Averaged results of 2-speaker separation on SMS-WSJ (6-channel input and loss), obtained by using TCN-DenseUNet [74] architecture.
Row	Systems	I	J	Loss	Val. set SDR (dB)	Test set
SDR (dB)	SI-SDR (dB)	PESQ	eSTOI
0a	Mixture	-	-	-	0.1	0.1	0.0	1.87	0.603
2a	UNSSOR	19	0	\( \mathcal{L}_{\text{MC+ISMS}} \)	11.1	10.7	9.7	2.92	0.770
2b	UNSSOR + Corr. based freq. align.	19	0	\( \mathcal{L}_{\text{MC+ISMS}} \)	11.1	10.7	9.7	2.92	0.770
2c	UNSSOR + Oracle freq. align.	19	0	\( \mathcal{L}_{\text{MC+ISMS}} \)	11.2	10.8	9.8	2.93	0.773
4a	PIT (supervised)	-	-	-	14.6	13.9	13.4	3.58	0.878
Table 10: Averaged results of 2-speaker separation on SMS-WSJ (6-channel input and loss), obtained by using DPRNN [75] architecture.
Row	Systems	I	J	Loss	Val. set SDR (dB)	Test set
SDR (dB)	SI-SDR (dB)	PESQ	eSTOI
0a	Mixture	-	-	-	0.1	0.1	0.0	1.87	0.603
2a	UNSSOR	19	0	\( \mathcal{L}_{\text{MC+ISMS}} \)	9.2	8.9	8.0	2.68	0.724
2b	UNSSOR + Corr. based freq. align.	19	0	\( \mathcal{L}_{\text{MC+ISMS}} \)	9.2	8.9	8.0	2.68	0.724
2c	UNSSOR + Oracle freq. align.	19	0	\( \mathcal{L}_{\text{MC+ISMS}} \)	9.3	9.0	8.1	2.68	0.727
4a	PIT (supervised)	-	-	-	12.3	11.7	11.3	3.00	0.820
DNN-estimated masks are used to mask spectral embeddings. In the six-channel case, the spatial embedding dimension is set to 360, following [84]. Differently from the Fig. 2 of [84], we do not use microphone-pair-wise Conv1D layers to obtain spatial embeddings. Instead, we obtain them by using a Conv1D layer with  $P (= 6)$  input channels and 360 output channels. We first use the DPRNN to obtain intermediate separation results in the time domain, and then apply STFT (with a window size of 32 ms, a hop size of 8 ms, and the square-root of Hann window) to obtain  $\hat{Z}(c)$  for each speaker  $c$ . The network is trained by only using the MC loss in (9) and not using the ISMS loss in (10). Although the ISMS loss is not used, we only observe minor frequency permutation on the SMS-WSJ dataset. All the other procedures are the same as the TF-GridNet based UNSSOR system. The results on SMS-WSJ, obtained by using six-channel input and loss, are presented in Table 10. We observe that UNSSOR also works with DPRNN to some degrees, and the results are not as good as the ones in Table 1.
L Comparison with MixIT
This section compares the results of UNSSOR with MixIT [30], which also deals with unsupervised separation. We put this comparison only in appendix, considering that MixIT may not be an ideal baseline to UNSSOR. Notice that MixIT needs to be trained on synthetic mixtures of mixtures (MoM), which ideally would require the two mixtures used in creating each MoM to be recorded in the same room using the same device. Differently, UNSSOR is designed to be trained directly on existing mixtures. In this case, the two models would be trained on different training examples, and this would make the comparison difficult. In the main body of this paper, we hence only consider methods that can be trained (or performed) directly on existing mixtures (such as IVA, spatial clustering and iRAS) as baselines.
To report the results of MixIT, we create a particular scenario (ideal for MixIT), where, for each existing SMS-WSJ mixture  $(y = x(1) + x(2) + n$  with  $n$  denoting noise), we randomly add two extra speakers in the same simulated room and use the same array placed at the same location so that we can have two 2-speaker mixtures (i.e., an existing SMS-WSJ mixture 1:  $y^{(1)} = x^{(1)}(1) + x^{(1)}(2) + n^{(1)}$  and a newly-simulated mixture 2:  $y^{(2)} = x^{(2)}(1) + x^{(2)}(2) + n^{(2)}$  where the superscript (1) denotes mixture 1 and (2) denotes mixture 2) to create an MoM for training MixIT for 4-speaker separation. Similarly to  $n^{(1)}$ ,  $n^{(2)}$  is sampled such that the SNR between  $x^{(2)}(1) + x^{(2)}(2)$  and  $n^{(2)}$  is in the range of [20, 30] dB. The DNN architecture and training configurations for MixIT are the same as that in UNSSOR and PIT. The loss function is defined similarly to (5) on the real, imaginary and magnitude of the reconstructed mixtures weighted by the summation of mixture magnitudes. Similarly to our proposed technique, we can feed 1-, 3- or 6-channel mixtures to TF-GridNet-based MixIT. At run time, the trained MixIT model is used to separate the existing two-speaker mixtures in SMS-WSJ to four outputs, and the two outputs with the highest energy are selected for evaluation.
Table 11: Averaged results of 2-speaker separation on SMS-WSJ, obtained by using MixIT [30].
Row	Systems	Val. set	Test set
SDR (dB)	SDR (dB)	SI-SDR (dB)	PESQ	eSTOI
0a	Mixture	0.1	0.1	0.0	1.87	0.603
5a	One-channel MixIT	6.7	6.6	6.3	2.21	0.691
5b	Three-channel MixIT	0.1	0.1	0.0	1.91	0.608
5c	Six-channel MixIT	8.2	8.0	7.8	2.43	0.745
This way, the evaluation scores can be pretty much directly compared with the ones obtained by UNSSOR.
The results are shown in Table 11. They are not as good as the ones reported in Table 1, 2, 3 and 4. For unknown reasons, three-channel MixIT fits the loss well but fails at separating speakers.