International Workshop on Acoustic Signal Enhancement (IWAENC2018), Sept. 2018, Tokyo, JapanONLINE INTEGRATION OF DNN-BASED AND SPATIAL CLUSTERING-BASEDMASK ESTIMATION FOR ROBUST MVDR BEAMFORMING
Yutaro Matsui 1,2 , Tomohiro Nakatani 1 , Marc Delcroix 1 , Keisuke Kinoshita 1 ,Nobutaka Ito 1 , Shoko Araki 1 , Shoji Makino 2
1

NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan
2
University of Tsukuba, 1–1–1 Tennodai, Tsukuba, Ibaraki, Japany.matsui@mmlab.cs.tsukuba.ac.jp, nakatani.tomohiro@lab.ntt.co.jp
test data. The SC-based approach [7,9,16–18], on the other hand, requires no prior training on the training data (for offline processing),
and can estimate masks from the test data in an unsupervised learning manner. On the assumption that the spatial features of speechand noise have different distributions, this approach finds these two
distributions based on the clustering of the spatial features, and themasks are estimated as the posteriors of each cluster at the corresponding time-frequency points. For these conventional approaches,both offline and online processing [9, 19–22] have already been formulated by researchers.
A joint optimization technique that integrates the above two approaches has also been proposed for offline processing [23, 24]. In[23], given a joint likelihood function for spectral features and spatial features, a mask estimation method was derived based on the
expectation-maximization (EM) algorithm. With the method, theinitial masks are first estimated based solely on a DNN, and then utilized by the SC-based mask estimation as the time-frequency dependent prior of the dominant sources to estimate the integrated masks.
Experiments showed that the integrated approach was more effective
than the two conventional mask estimation approaches.
This paper extends the above joint optimization approach toonline mask estimation by integrating the two conventional online
mask estimation approaches. This extension is important, for example, to provide frontend for low latency distant speech recognition,such as ASR in smart speakers. In the integrated framework, while
the two conventional approaches perform online mask estimation inparallel, the integration is conducted at each time segment in a way
similar to that used for the offline integration framework. In addition, this paper presents an effective way of initializing the model
parameters of SC for online processing without prior knowledgeof the speaker location, which has been difficult with conventional
SC-based online mask estimation. In the experiments, an MVDR
beamformer is performed based on the estimated masks and theresult is evaluated using the real data of the CHiME-3 multichannel
noisy speech corpus [25]. The proposed method is shown to greatly
outperform the two conventional online approaches.
In the remainder of this paper, two conventional online mask
estimation methods are described in Section 2, and the proposed
method is presented in Section 3. Sections 4 and 5, respectively,
provide experimental results and concluding remarks.

ABSTRACTThis paper discusses the online estimation of time-frequency masks,
which enables us to perform mask-based beamforming by online
processing for robust automatic speech recognition (ASR). Two approaches to online mask estimation have been separately developed
for this purpose. One is based on a deep neural network (DNN),
which exploits the spectral features of the signal. The other is basedon spatial clustering (SC), which exploits the spatial features of thesignal. This paper proposes a new method that integrates the two
online estimation approaches to further improve online mask estimation by exploiting the advantages of both approaches. Experiments
using the real data of the CHiME-3 multichannel noisy speech corpus show that the proposed method greatly outperforms the conventional approaches in terms of improving the word error rate (WER).
Index Terms— Beamforming, time-frequency masking, online
processing, DNN, spatial clustering
1. INTRODUCTION
When our daily speech is captured using distant microphones, various types of ambient noise are mixed with the captured signals,
and severely degrade the ASR performance. To solve this problem, beamforming is being extensively studied as a noise reduction
frontend for ASR. Delay-and-sum beamforming, minimum variance distortionless response (MVDR) beamforming, and maximum
signal-to-noise ratio beamforming are often employed [1–3], andhave been shown to improve the ASR performance in tasks ranging from medium vocabulary distant speech recognition [4] to large
vocabulary meeting transcription [5, 6].
Accurate estimation of captured signal characteristics, such as
the spatial covariance matrices of the speech and the noise, is crucial for performing effective beamforming. For this purpose, researchers have recently proposed time-frequency mask-based beamforming approaches [7–11]. The central idea is to leverage the spectral sparsity of speech signals by using time-frequency masks that
represent the probability of speech (or noise) dominating the corresponding time-frequency points [12–14]. Then, the spatial covariance matrices of the speech and the noise are estimated solely fromthe time-frequency masks and the captured signal, and used for constructing beamformers.
The two main techniques proposed for mask estimation are the
DNN-based and SC-based approaches. With the DNN-based approach [10, 11, 15], a DNN is trained in advance on training data sothat it can estimate masks from the spectral features of a noisy speech
signal. Then, the trained network is used to estimate the masks for2. CONVENTIONAL ONLINE MASK ESTIMATIONLet us assume that a single speech signal is captured by multiple microphones with certain additive diffuse noise. Then, the goal of thispaper is to present a method for estimating masks at individual time71c
978-1-5386-8151-0/18/$31.00 ⃝2018IEEE

International Workshop on Acoustic Signal Enhancement (IWAENC2018), Sept. 2018, Tokyo, JapanR0f,l−1 , R1f,l−1

frequency (TF) points in the short-time Fourier transform (STFT)
domain that represents the probability of speech (or noise) dominating the respective TF points. With accurate mask estimates, we candesign an effective beamformer to suppress the noise [9].
In the following, we first describe the two conventional online
mask estimation methods, one based on SC [9] and the other basedon DNN [19]. Then, in the next section, we present our proposed
method, which integrates the two methods.

yn,fn ∈ BlFigure 1 illustrates the processing flow of SC-based online mask estimation. In the flow, spatial features, Xn,f , are first extracted fromthe captured signal at individual TF points as

Xn,f

d,SC
- Mn,f- LSTMYn,mnetworkd,DNN- Mn,fis performed for each minibatch one after another. Hereafter, eachminibatch is denoted by Bl where l is the index of the minibatch,and is assumed to be composed of a set of time frames included in
the corresponding segment. To accumulate the information extracted
from each minibatch, the model parameters, {Rdf,l−1 }, estimated ina minibatch, Bl−1 , are passed to the next minibatch, Bl , and used
for the optimization with a certain forgetting factor. (See [9] for thedetails of the online estimation.)
Note that the initial values of Rdf,0 have to be set appropriately
for the first minibatch, B1 , in the above online estimation because
otherwise the convergence is not very fast and it is very difficult
to solve the permutation problem between speech and noise in an
online processing manner. This requires the speaker location to beroughly fixed in advance and the initial values of R1f,0 to be given
for the location, which greatly limits the applicability of the online
algorithm. For example, it was assumed for the CHiME-3 challengethat the speaker speaks while facing a tablet equipped with a set of
microphones.

(1)
(2)

2.2. DNN-based online mask estimation
wfd A(Xn,f |dn,f = d; θSC ),Figure 2 illustrates the processing flow of the DNN-based mask estimation. A bi-directional long short-term memory (BLSTM) network
is widely used for DNN-based mask estimation [10], however, it cannot be applied to online processing due to its backward propagation
path. Instead, by dropping the backward path from the BLSTM network, we obtain a long short-term memory (LSTM) network thatcan be applied to online mask estimation [19]. The LSTM network
receives the spectral features of the signal, defined in eq. (6), andestimates masks in a frame-by-frame processing manner.

(3)

d=0

A(X|dn,f = d; θSC ) =- cACGMM

Fig. 2. Processing flow of DNN-based online mask estimation

where yn,f,m is an STFT of the captured signal obtained by the m-th
microphone (1 ≤ m ≤ Nm ) at time n (1 ≤ n ≤ Nt ) and frequency
f (0 ≤ f ≤ Nf ), yn,f is a vector containing yn,f,m for all the microphones, T denotes non-conjugate transposition, and ∥·∥ denotes
the Euclidean norm. If we assume that each TF point is dominatedby either speech or noise according to the spectral sparsity of speech,
Xn,f at each TF point has a spatial characteristic approximately corresponding to that of the dominant source. Then, by clustering the
TF points based on the spatial features, we can obtain two clusters,
one dominated by speech and the other dominated by noise. We canthen estimate the masks using these clusters as a basis.
For the clustering, a complex angular central Gaussian mixture
model (cACGMM) has been shown to be effective for modeling the
distribution of Xn,f [18,26]. The model is defined at each frequencyf asp(Xn,f ; θSC ) =- log | · |0 ≤ f ≤ Nf

2.1. Spatial clustering-based online mask estimation

1∑∥yn,f ∥

Fig. 1. Processing flow of SC-based online mask estimation
yn,f,myn,f = [yn,f,1 , · · · , yn,f,M ]T ,yn,fXn,f =,∥yn,f ∥- yn,f(Nm − 1)!
1,
2π Nm det Rdf (X H (Rdf )−1 X)Nm
(4)

where a random variable dn,f , referred to as a dominant source index, indicates whether speech or noise dominates each TF point(dn,f = 0 for noise and 1 for speech), wfd is the mixture weight,
which corresponds to the prior of dn,f , i.e., wfd = p(dn,f = d), andA(X|d; θSC ) is the conditional distribution of X given d, definedas the complex angular central Gaussian (cACG) distribution. Rdf isa shape parameter of a cACG distribution, which is characterized byan Nm × Nm positive definite Hermitian matrix and roughly corresponds to the spatial covariance matrix of a signal, and H denotes
conjugate transposition. With this model, clustering is performed byestimating a set of model parameters, θSC = {{wfd }, {Rdf }}, basedon maximum likelihood estimation. An efficient estimation methodd,SCcan be derived based on the EM algorithm. Then, the masks, Mn,f,are estimated as the posterior probability of individual TF points being dominated by the noise (d = 0) and by the speech (d = 1), i.e.,
d,SC
Mn,f
= p(dn,f = d|Xn,f ; θSC ).
Online mask estimation method can be formulated based on the
online EM algorithm [9]. With the method, the captured signals are
separated into short segments of the order of hundreds of milliseconds, which are referred to as minibatches, and the EM algorithm

Yn,f,m = log |yn,f,m |[]Yn,m = Yn,0,m , · · · , Yn,Nf ,m

(5)
(6)

The LSTM network is composed of an LSTM layer followed bythree feed-forward neural network layers. To train the network, we
adopted the same scheme as used in [10], except that batch normalization was not performed as it is unsuitable for online processing.
The desired output is a concatenation of two types of ideal binarymasks, one for speech and the other for noise. The ideal binarymasks for speech (or those for noise) take 1 when the TF points
are dominated by the speech (or noise) and take 0 otherwise. Accordingly, the trained LSTM network outputs two different masks,
d,DNN
Mn,f
for d = 0 and 1, respectively, for the noise and the speech.
Note that, to obtain ideal binary masks, only simulated data canbe used as the training data, from which we can extract the microphone images of speech and noise separately. So, the LSTM network
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International Workshop on Acoustic Signal Enhancement (IWAENC2018), Sept. 2018, Tokyo, Japan

performance often deteriorates when estimating masks for real datathat are recorded in real acoustic environments because of the mismatch between simulated and real data.

yn,f,mn ∈ BlFor the integration of the two online mask estimation methods, we
use the same scheme as one adopted for an offline integration framework [23]. For this purpose, we first introduce the following joint
likelihood function.

d,DNN
Mn,f

- yn,f?
cACGMM-Xn,f∥yn,f ∥d,INT
- Mn,fFig. 3. Processing flow of integrated online mask estimation

where Xl and Yl are sets of Xn,f and Yn,m s.t. n ∈ Bl′ and1 ≤ l′ ≤ l, and θDNN is a set of parameters for the LSTM networkthat are assumed to be fixed by pre-training. Then, with proper assumptions as regards conditional independence over time-frequency
points that are commonly used in the conventional approaches [27],the above function is rewritten, disregarding constant terms, as∏∑
L(θSC ) =p(Xn,f |dn,f = d; θSC )p(dn,f = d|Yl ; θDNN ),where
Rdf,new = Nm

∑

d,INT
H
Mn,f
Xn,f Xn,f()−1
Hn∈Bl Xn,f
Rdf,l
Xn,f

(b) In E-step:
(

)

d,DNN
Mn,f
A Xn,f |d; θSC
d,INT
Mn,f
= ∑
d,DNN
A (Xn,f |d; θSC )d Mn,fdwhere p(Xn,f |dn,f = d; , θSC ) corresponds to the cACG distribution in eq. (3) and p(dn,f = d|Yl ; θDNN ) can be interpretedd,DNN
as the masks Mn,fobtained by the LSTM network. With thisinterpretation, the above equation can be viewed as a variation ofcACGMM with a time-frequency dependent mixture weight, definedd,DNN
as Mn,f. Accordingly, the EM algorithm can be applied in thesame way as SC-based online mask estimation [9] for the estimationd,INTof θSC and the integrated masks, Mn,f
. In particular, because
d,DNN
Mn,f
is obtained by online processing, we can also formulate an
online EM algorithm for the integrated method.

In our experiments, we set the number of EM iterations at one foreach minibatch.
3.2. Initialization of shape parameters
In the above online processing, the shape parameters, Rdf,0 , and thecumulative sum of the masks, Λdf,0 , must be initialized at the beginning. It is natural to set Λdf,0 = 0 and to obtain R0f,0 for noise fromthe test data when speech is absent. In contrast, the effective initialization of R1f,0 for speech is the key to successful online estimation.
This paper prepares the following three methods to achieve this, andcompares them experimentally in the next section.

3.1. Integrated online mask estimation
Figure 3 shows the processing flow of the proposed online mask esd,INT
timation method. In the flow, the integrated mask, Mn,f
, in eachminibatch Bl is estimated as follows:• PreTrained: R1f,0 are trained in advance using certain
training data that contain utterances from a pre-determined
speaker location without additive noise. This method is identical to that used for the SC-based online mask estimation [9].

1. The LSTM network first receives the spectral features, andd,DNNestimates the masks, Mn,fin the minibatch Bl .

• NoPrior: R1f,0 are set as identity matrices.

2. The cACGMM receives the spatial features, Xn,f , the shapeparameters, Rdf,l−1 , estimated in the previous minibatch,d,DNN
and Mn,f. It then sets the initial values of the integratedd,INTmasks, Mn,f, the shape parameters, Rdf,l , and the cumulative sum of the masks, Λdf,l as• PostTrained: R1f,0 are initialized in the same way as Nod,INT
Prior. However, Rdf,l are not used for estimating Mn,fatearly minibatches until they have been reliably trained usingd,INT
the test data. With the early minibatches, Mn,fis subd,DNN
dstituted with Mn,f . Rf,l are determined to be reliable
when Λ1f,l exceeds a certain predetermined threshold because
Λ1f,l indicates the number of TF points that are dominated byspeech and included in current and past minibatches.

d,INT
d,DNN
Mn,f
= Mn,f,∑network

R0f,l−1 , R1f,l−1

L(θSC ) = p(Xl , Yl ; θDNN , θSC ),Λdf,l = Λdf,l−1 +- LSTMn ∈ Blyn,fRdf,l = Rdf,l−1 ,Yn,m0 ≤ f ≤ Nf

3. PROPOSED ONLINE MASK ESTIMATIONn,f- log | · |

d,DNN
Mn,f
.

Note that no prior training on Rdf,0 is required for NoPrior or PostTrained, and this is beneficial in terms of widening the applicabilityof SC-based online mask estimation.

n∈Bl
d,INT
d
3. The cACGMM updates Rf,land Mn,f
by using EM iterations as shown below, and outputs both of them.

4. EXPERIMENTAL EVALUATION(a) In M-step:

We conducted ASR experiments using the CHiME-3 Speech Separation and Recognition Challenge corpus [25] to evaluate the noise
reduction performance of our proposed approach. The corpus wascreated by using a six-channel microphone array attached to a tabletΛdf,l−1 d1Rdf,l =
Rf,l−1 + d Rdf,new
Λdf,l
Λf,l

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International Workshop on Acoustic Signal Enhancement (IWAENC2018), Sept. 2018, Tokyo, Japan

Table 1. The number of utterance data in the CHiME-3 corpus.
simulated
real data (simu) data
Training set
1600
7138
Development (dev) set
1640
1640
Evaluation (eval) set
1320
1320

Table 3. WERs (%) obtained using different online mask estimators.
cACGMM and Proposed both used “PreTrained” shape parameters
for shape parameter initialization. Note that the WERs (%) obtained
when we performed offline processing with the BLSTM network,cACGMM, and the integrated approach were 7.34, 8.28, and 7.10,
respectively, for real data of the eval set.
deveval
Ave simu real
Ave
simu
real
No frontend 8.62 8.24 9.01 12.89 10.17 15.60
LSTM
5.33 5.33 5.33
8.14
6.59
9.69
cACGMM
5.60 5.98 5.21
9.20
8.86
9.53
Proposed
4.98 5.33 4.63
7.35
6.81
7.89

Table 2. Experimental conditions.
Sampling frequency
16 kHzFrame length
64 ms
Frame overlap
75%
Window function
Hanning
Number of EM iterations
1
Number of microphones
6

Table 4. WERs (%) obtained using online mask estimation methods,
LSTM and two proposed methods (NoPrior and PostTrained). No
method requires prior training of the shape parameters.
Set
Bus
Caf
Ped
Str
Ave
LSTM
6.34
5.32 4.69 4.96 5.33
NoPriordev
6.06
4.76 4.59 4.59 5.00
PostTrained
5.96
4.68 4.48 4.62 4.94
LSTM
13.48 9.81 8.00 7.45 9.69
NoPrioreval 12.15 8.42 6.86 6.65 8.52
PostTrained
11.09 8.48 7.29 6.65 8.38

device. The recordings were obtained in four different noisy environments, i.e., public transport (Bus), cafe (Caf), pedestrian area (Ped),and street junction (Str), and they feature several male and female
speakers uttering sentences from the Wall Street Journal (WSJ) [28].
The corpus is divided into three individual subsets as Table 1, eachcontaining both real data and simulated (simu) data.
For the evaluation, we employed a mask-based MVDR beamformer that was used for evaluating offline/online SC-based mask
estimation methods in [9, 23], and used the speech recognizer described in [29], which was referred to as 1-pass SI and based on amulti-condition convolutional neural network (CNN) acoustic modeland a recurrent neural network (RNN) language model.

uses the integrated masks from the beginning of the online estimation, PostTrained started using them only after the cumulative sumof the masks obtained by LSTM exceeded a certain predeterminedthreshold. In the experiment, the threshold was set at 1.5 because it
achieved the best WER for the dev set. Other hyperparameters are
the same as those used for the experiments in section 4.1. Note thatthe cACGMM is not included in this comparison because it cannot
perform online mask estimation without prior training.
Table 4 summarizes the WERs obtained in the experiments. We
can see that the two proposed methods without any prior training of
the shape parameters consistently outperformed LSTM. This meansthat the integration could successfully improve the accuracy of the
LSTM-based online mask estimation by incorporating the SC-based
online mask estimation with no prior training. When we compare
the two proposed methods, PostTrained was slightly better than NoPrior.

4.1. Evaluation of online processing methods
We compared three online mask estimation methods, i.e., the DNNbased method using the LSTM network (LSTM), the SC-based
method using cACGMM (cACGMM), and our proposed method,which integrates the two methods (Proposed). We used “PreTrained”
shape parameters for the initialization of both cACGMM and Proposed. R1f,0 was trained on speech signals recorded in a quiet boothand R0f,0 was trained on signals that only contained noise. Bothdata are included in the CHiME-3 corpus. Also, based on the experimental setting adopted in [9], we set the size of the first minibatchat 500 ms and that of succeeding minibatches at 250 ms to ensurethat the first minibatch contained the target speech signal. Other
hyperparameters were set as shown in Table 2.
Table 3 summarizes the WERs obtained in the experiments. In
the table, the proposed method outperformed cACGMM for both thereal and simu data, and outperformed LSTM for the real data. Although LSTM was slightly better than the proposed method for thesimu data, this is because there are almost no mismatches between
the training and test sets in the simu data. Note that the performanceon the simu data was not considered an official performance metricin the CHiME-3 challenge. Hereafter, we only focus on the performance on the real data.

5. CONCLUDING REMARKSThis paper proposed a new online mask estimation technique that integrates two conventional online mask estimation methods, one using an LSTM network and the other using a cACGMM-based SC.
The integration is formulated based on a joint likelihood function
defined on two different features of the signal, namely the spectral
and spatial features, and is performed by optimizing the joint function using the online EM algorithm. The proposed method can improve the mask estimation accuracy by exploiting the two different
features simultaneously, and it enables SC-based mask estimation
to realize online processing with no prior training on the shape parameters of the cACGMM. Experiments using real data from theCHiME-3 multichannel noisy speech corpus showed that the proposed method greatly outperformed the conventional approaches interms of WER reduction when it was combined with mask-based
MVDR beamforming.

4.2. Evaluation of shape parameter initialization methods
Next, we compared three online estimation methods that do not require pre-trained shape parameters of the cACGMM, i.e., LSTM, theproposed method with NoPrior (NoPrior), and that with PostTrained(PostTrained). With both NoPrior and PostTrained, the shape parameters are initialized as the identity matrices. Whereas NoPrior
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