# Acoustic Echo Cancellation using Residual U-Nets

J. Silva-Rodríguez(1), M.F. Dolz(2), M. Ferrer(3), A. Castelló(2), V.
Naranjo(4), G. Piñero(3) (1) Inst. Transport and Territory, Universitat
Politècnica de València<EMAIL_ADDRESS>
(2) Dept. Ingeniería y Ciencia de los Computadores, Universitat Jaume I,
<EMAIL_ADDRESS>
(3) ITEAM, Universitat Politècnica de València<EMAIL_ADDRESS>
(4) Inst. Invest. Innov. Bioingeniería, Universitat Politècnica de València,
<EMAIL_ADDRESS>
###### Abstract

This paper presents an acoustic echo canceler based on a U-Net convolutional
neural network for single-talk and double-talk scenarios. U-Net networks have
previously been used in the audio processing area for source separation
problems because of their ability to reproduce the finest details of audio
signals, but to our knowledge, this is the first time they have been used for
acoustic echo cancellation (AEC). The U-Net hyperparameters have been
optimized to obtain the best AEC performance, but using a reduced number of
parameters to meet a latency restriction of $40$ ms. The training and testing
of our model have been carried out within the framework of the ICASSP 2021 AEC
Challenge organized by Microsoft. We have trained the optimized U-Net model
with a synthetic dataset only (S-U-Net) and with a synthetic dataset and the
single-talk set of a real dataset (SR-U-Net), both datasets were released for
the challenge. The S-U-Net model presented better results for double-talk
scenarios, thus their inferred near-end signals from the blind testset were
submitted to the challenge. Our canceler ranked 12th among 17 teams, and 5th
among 10 academia teams, obtaining an overall mean opinion score of $3.57$.

##  I Introduction

The video conferencing global market size was valued at $3.85 billion USD in
2019 and is predicted to significantly grow during 2020 due to the COVID-19
pandemic [1]. From the point of view of the customers, one of the most
annoying experience is to hear their own echo from the other side of the line
due to a malfunctioning of the acoustic echo cancellation (AEC) system.

The basic theory of AEC systems was first developed by AT&T Bell Labs [2], and
it was one of the earliest applications of adaptive filters [3]. The aim is to
suppress the undesired echo produced by the acoustic coupling between a
loudspeaker and a microphone, usually driven by the same device. However,
advanced AEC systems are also capable of reducing background noise and
enhancing the near-end speech captured by the microphone [4]. Traditionally,
echo cancellation is accomplished by adaptively identifying the echo path
impulse response between the loudspeaker and the microphone and subtracting
the estimated echo signal from the microphone signal. In practice, the
canceler must also deal with non-stationary scenarios and non-linear effects
over the signals. To combat these impairments, residual echo suppressors (RES)
[5, 6] and voice activity detectors for the far-end and near-end signals s [7,
8, 9] are usually included in the AEC system design.

Recently, AEC solutions have been proposed from the machine learning (ML)
perspective. On the one hand, there are works that use neural networks in
combination with classical adaptive cancelers in order to detect double-talk
situations [9], to model the spectrum of the residual echo [10], or to
suppress it [11, 12]. On the other hand, complete AEC solutions implemented by
means of deep learning models can be found in the recent literature. In [13]
the authors approach the echo suppression as a source separation problem,
proposing a recurrent neural network with bidirectional long short-term memory
(BLSTM) to estimate the best ratio mask in double-talk scenarios. Same authors
propose in [14] to add a convolutional recurrent network (CRN) to estimate the
near-end speech, and in parallel they use a LSTM to detect its presence. In
[15], deep gated recurrent neural networks are proposed to estimate the near-
end signal and the echo using multitask learning, and in [16] a hybrid AEC
combining a classical adaptive filter and a contextual attention module (CAM)
is used.

There is no definite AEC solution and the problem is still open. For this
reason, Microsoft has organized the ICASSP 2021 Acoustic Echo Cancellation
Challenge “to stimulate research in the area of AEC” [17, 18]. We describe in
this work an AEC system based on Residual U-Nets, which has achieved an
overall subjective score of $3.57$ in the challenge final results [18]. The
U-Net architecture was firstly proposed in [19] to solve image segmentation
problems in medical applications, but they have also been used to separate the
singing voice from the background sounds [20, 21], given its capacity for
recreating high-quality audio. However, to our knowledge, this is the first
time that U-Nets are used for acoustic echo cancellation. The rest of the
paper is as follows: we describe our proposed AEC system in Section II,
including an optimization of the U-Net main hyperparameters. A brief
description of the databases released by Microsoft for the challenge [17] and
several experimental results are shown in Section III, whereas Section IV
summarizes the main conclusions.

##  II Proposed model

The methodological core of the proposed approach is a U-Net convolutional
neural network (CNN) [19] able to suppress the echo in different communication
scenarios. In the context of the AEC, the microphone signal $y(n)$ is a
mixture of the original audio $s(n)$, so-called near-end speech, and the far-
end speech $x(n)$ such that:

$y(n)=s(n)+f(x(n))+v(n)=s(n)+d(n)+v(n)\,,$ (1)

where $v(n)$ denotes background noise and $f(\cdot)$ is the function modeling
the transformation of the far-end speech signal emitted by the loudspeaker and
captured by the microphone, i.e., the echo $d(n)$. This model describes a
double-talk scenario where $s(n)$ is present in $y(n)$, whereas a single-talk
scenario is denoted when $s(n)=0$. It is usually assumed that $f(x(n))$ is a
linear function such that $d(n)=h(n)\ast x(n)$, where $h(n)$ is the room
impulse response (RIR) between the loudspeaker and the microphone, but we will
also consider non-linear effects on $x(n)$, as clipping or impairments due to
time-varying RIRs.

###  II-A Feature extraction

The U-Net model takes as input pre-processed segments of the far-end speech,
$x(n)$, and the microphone signal, $y(n)$, and aims to apply a deep filtering
function to infer the estimated near-end speech, $\hat{s}(n)$. The proposed
AEC pipeline uses $160$-ms time frames of both $y(n)$ and $x(n)$. The time
frame is formed by stacking $T_{f}=40$ ms of new speech samples, and $120$ ms
of previous samples to ensure the preservation of the temporal information.
Then, their short-time Fourier transforms (STFT) are obtained using a Hanning
window of size $W=318$ samples and $75\%$ of overlap. Normalized magnitude
spectrograms of size $160\times 32$ from the near-end microphone signal, $Y$,
and from the far-end speech, $X$, are then obtained and used as input features
to the model, as it is shown in Fig. 1. During the inference stage, the
normalization process is inverted to obtain the magnitude of the predicted
near-end speech spectrogram, $\hat{S}$. Then the preserved phase information
of the $Y$ spectrogram is added to $\hat{S}$, and their inverse STFT is
calculated. The newest period of $40$ ms samples is then selected to build the
estimated near-end speech $\hat{s}(n)$. In case of single-talk scenarios where
$s(n)$ is not present, the output of the model would not have any content,
although we denote their spectrogram as $\hat{S}$ for the sake of clarity.

###  II-B Residual U-Net

As shown in Fig. 1, the U-Net is composed of two branches: the encoder,
responsible of obtaining relevant features from the image, and the decoder, in
charge of the construction of the target image. The encoder branch consists of
stacked convolutional blocks with increasing number of filters and dimensional
reduction via the max-pooling operator. In the decoder branch, the
convolutional blocks are followed by deconvolutions that progressively recover
the spatial dimensions and reduce the number of filters to half. In order to
combine multi-scale information, the encoder is connected via shortcut
connections to the decoder.

Figure 1: Residual U-Net proposed for acoustic echo cancellation.

In our model, the spectrograms of the near-end microphone, $Y$, and the far-
end speech, $X$, are used as inputs in a two-channel grayscale image of size
$160\times 32\times 2$. The initial number of filters used, $F_{0}$, has been
optimized as described in Section II-D. It is noteworthy to mention that the
number of filters used is smaller than usual due to the strict rules of the
AEC Challenge regarding real-time implementation [17]. With respect to the
pooling/deconvolution operations, the pooling is performed only in the
frequency-related dimension of the spectrogams using filters of size $2\times
1$. Due to the small size of the input time frames, a temporal pooling could
soften the spectrogram and degrade the speech representation. As model output,
a convolutional layer of size $1\times 1$ reconstructs the estimated near-end
speech spectrogram, $\hat{S}$.

The convolutional block in charge of the feature extraction (blue connections
in Fig. 1) is designed using residual connections in the Residual U-Net. These
connections have shown to improve deep learning models optimization, avoiding
gradient vanishing problems in other U-Net applications [22, 23]. In our
model, this block is composed of consecutive convolutional layers with size
$3\times 3$ and ReLU activation. The first layer standardizes the activation
maps to the amount of filters corresponding to the depth level. The resultant
activation maps from this layer are combined in a shortcut connection with the
results of $N$-stacked convolutional layers via an adding operation. The
number of stacked filters is empirically fixed during the hyperparameters
tuning experiments described in Section II-D.

###  II-C Loss estimation

During the training stage, learning is driven by the comparison between the
spectrograms of the predicted and the real (ground truth) near-end signals.
Let us denote the spectrogram of the target signal as $S(m,k)$, where $m$
denotes the indices of the $M$ subframes of $W$ samples in $T_{f}$, and $k$
being the frequency bin, $k=0,1,\ldots,W/2$. The objective function is defined
as the root-mean-square error between the spectrograms:

$L=\sqrt{\frac{1}{M(W/2+1)}\sum_{m\in
T_{f},k}\left(\hat{S}(m,k)-S(m,k)\right)^{2}}\,.$ (2)

Thus, the network focuses on learning to filter the last $T_{f}$ time period
of the near-end speech. Otherwise, the model could get stacked on a local
minimum filtering the signal corresponding to the whole $160$ ms frame.

###  II-D Tuning the U-Net model

Given the large number of U-Net variants, we have performed a hyperparameter
optimization search to select the model configuration that reduces both the
model loss (2) and the computational complexity of the inference stage, so as
to accomplish the real-time requirements of the AEC Challenge. For this
purpose, we have used Hyperas [24], a hyperparameter optimization tool for
Keras models that operates on top of Hyperopt [25]. An advantage of Hyperas is
that, instead of using a grid-based search, it leverages Bayesian algorithms
to partially search the parameter space for relatively optimal parameter
settings.

TABLE I: Values per each hyperparameter analyzed with Hyperopt for the U-Net model. Hyperparameter | Values
---|---
Optimizer | Nadam, SGD, Adam
Learning rate |  $10^{-3}$, $10^{-4}$, $10^{-5}$
# Encoders-Decoders | 4–3, 3–2
Residual blocks | Conf#1, Conf#2
# Filters ($F_{0}$) | 8, 16

Table I displays the set of hyperparameters considered in the U-Net
optimization. Specifically, we tested three different optimizers, namely SGD
[26], Nadam, and Adam [27], each with three different learning rate values, as
shown in the second row of Table I. Besides, we considered the number of
encoders and decoders of the U-Net (third row), and two different
configurations for the residual blocks (fourth row): “Conf#1” stacks two
convolutional layers followed by the shortcut connection carrying the input,
whereas “Conf#2” follows the same principle as “Conf#1”, but adds a
convolution operation in the shortcut prior to the addition of the residual
connection to the main branch. The last hyperparameter (fifth row) considers
two baseline values for the number of filters, $\\{8,16\\}$, scaled by a
factor of $2^{i}$ and $2^{(n-i+1)}$ in the $i$-th encoder and $i$-th decoder
of a U-Net, respectively. The result of this hyperparameter search revealed
that the minimum loss was achieved with the Nadam optimizer using a learning
rate of $10^{-4}$ on a U-Net consisting of 16 baseline filters, 4 encoders, 3
decoders and residual blocks according to “Conf#1”.

##  III Experiments and Results

###  III-A Real-time performance evaluation

Once hyperparameters of the U-Net model were selected, we set up our target
platform, a personal computer equipped with an Intel Core i5 quadcore running
at 2.3 GHz, to measure the inference time while using the TensorFlow Lite
framework v2.0.1111https://www.tensorflow.org/lite. As stated in the AEC
Challenge rules [17], any proposed AEC system should take less than the stride
time (in our case $40$ ms) to process a frame, and the total algorithmic
latency allowed should be $\leq 40$ ms. Using 32-bits floating-point (FP32)
arithmetic, the inference time was $45.63$ ms, which exceeded the allowed
latency. Then, we quantized the U-Net to use half-precision arithmetic (FP16).
This allowed for a reduction of $52.5$% with respect to using FP32. Table II
shows the time breakdown for the inference operation given an input frame when
using FP16 (w/ quantization) and FP32 (w/o quantization).

TABLE II: Time breakdown of the complete inference operation. | Time w/ | Time w/o
---|---|---
Stage | quantization (ms) | quantization (ms)
Get buffer ($40$ ms) | $~{}~{}0.003$ | $~{}~{}0.003$
Data preparation | $~{}~{}1.193$ | $~{}~{}1,193$
Model inference | $18.953$ | $42.903$
Data extraction | $~{}~{}1.531$ | $~{}~{}1.531$
Total | $21.68$ | $45.63$

###  III-B Datasets

Two training datasets were released by Microsoft for the AEC Challenge222For
further details see [17]. The AEC Challenge datasets are downloadable from:
https://github.com/microsoft/AEC-Challenge:

1) Real dataset: This dataset consists of more than $2{,}500$ different real
environments, audio devices, and human speakers in single-talk and double-talk
scenarios with time-varying and stationary RIRs. Clean near-end speech
segments were also provided, although they did not correspond to the near-end
speech content present in the double-talk signals, i.e., there was no ground
truth to feed the loss function (2).

2) Synthetic dataset: It consists of $10{,}000$ synthetic samples representing
single-talk, double-talk, near-end noise, far-end noise, and various nonlinear
distortion situations. Each sample includes a far-end speech, echo signal,
near-end microphone signal and the near-end speech that will be considered the
ground truth $S$ in (2). Further details on each generated sample are
available in the “meta.csv” file available in the repository regarding the
signal-to-echo-ratio (SER), the addition of noise to the near-end and far-end
signal, and the non-linearity of the RIR. For the $10{,}000$ synthetic
samples, the SER was uniformly distributed between $-10$ and $10$ dB in steps
of $1$ dB.

Microsoft also released a Testset at the beginning of the challenge consisting
of approximately 800 recordings partitioned into the following subsets:
“Clean”, formed by clean speech with a Mean Opinion Score (MOS) $>4$ for both
near and far-end signals, and “Noisy”, formed by noisy speech for both near
and far-end signals. The signal conditions of this testset were similar to the
blind testset used by Microsoft for the evaluation of the AEC Challenge,
although the speakers and spoken texts were different. The estimated near-end
signals inferred by the baseline model [28] were also released and we have
used them as a reference for the performance comparison of the preliminary
results presented in Section III-D.

###  III-C Performance metrics

We have used the echo return loss enhancement (ERLE) and the perceptual
evaluation of speech quality (PESQ) [29] as the performance metrics. The ERLE
evaluates the echo reduction achieved during single-talk periods, when the
near-end signal $s(n)$ is not present:

$\mathrm{ERLE}=10\log_{10}\frac{E[y^{2}(n)]}{E[\hat{s}^{2}(n)]}\approx
10\log_{10}\frac{\sum_{n}y^{2}(n)}{\sum_{n}\hat{s}^{2}(n)}$ (3)

where the statistical expectation operation $E[\cdot]$ is estimated by the
mean square value over the whole speech duration. The PESQ is an objective
measure of the quality of the estimated near-end speech $\hat{s}(n)$ compared
to that of the original speech $s(n)$, thus it will be used for double-talk
scenarios. The PESQ scores range from $-0.5$ to $4.5$, and a higher score
indicates a better speech quality.

###  III-D Preliminary Results

We have trained the optimized U-Net model of Section II-D with the training
datasets described above generating two different echo cancelers:

S-U-Net: Only the synthesized dataset has been used to train. A $10$% of the
dataset not used for training has been selected to be used as a testset in the
performance analysis.

SR-U-Net: The synthesized dataset and the single-talk signals of the real
dataset have been used to train the U-Net. The ground truth for the real
dataset is a silence, that is, the pixels of the $S$ spectrogram all have a
$0$ value.

We have also implemented a classic adaptive AEC filter of $4{,}000$
coefficients that uses a Partitioned Frequency-Block Least Mean Square (PFB-
LMS) algorithm [30]. The block size was 1024 and the step size was
$\mu=0.0001$. In case the algorithm starts to diverge, the step size is
reduced by half.

The ERLE values of the different methods are shown at the upper part of Table
III, where we have also included the results for the Testset baseline signals
provided by Microsoft. For the synthetic testset, a mask from the near-end
speech has been calculated to select the single-talk periods of the audio
clips. In the case of the Testset (TS), only the signals labeled as single-
talk have been used. It can be noticed that the ERLE levels shown by the SR-U-
Net model on the Testset signals double the Baseline and S-U-Net levels, which
is a result of the addition of the real single-talk signals to the training
set. Regarding the PFB-LMS, it cannot reduce the echo more than a few dB in
all cases.

Regarding the PESQ evaluation, it has been carried out for the synthetic
testset taking the near-end signal (ground truth) as the reference. The
unprocessed PESQ for the microphone signal has also been calculated as in
[15], and the gain of each method with respect to the unprocessed PESQ is
shown at the lower part of Table III. A mask from the near-end speech has been
calculated to select the double-talk periods of the audio clips. On the one
hand, it can be appreciated how the extended training of the SR-U-Net model
deteriorates its performance in double-talk scenarios. Therefore, the S-U-Net
is preferred for its highest quality of the inferred near-end speech.
Spectrograms in Fig.2 illustrate an AEC example on a signal from the synthetic
testset. The microphone signal was formed using a nonlinear RIR, adding noise
at the far-end, and adding the echo signal with a SER level is $1$ dB. The
near-end speech estimated by the S-U-Net achieved an ERLE of $39.9$ dB and a
PESQ score of $3.07$.

Figure 2: Spectrograms of microphone, estimated near-end, and near-end signals
of synthesized sample #$2477$.

On the other hand, the PFB-LMS method achieves a PESQ gain similar to the S-U-
Net, which does not correspond to the perceived quality when listening to
their resulting inferred signals. It can be explained because the PESQ method
[29] was proposed to measure the distortions produced by a voice-over-IP
network on a coded speech signal, but it is still in discussion its validity
to measure the subjective perception when several speech signals are present
[31].

TABLE III: ERLE values and PESQ gains of the AEC methods.

| Method | Synthetic | “Clean” TS | “Noisy” TS
---|---|---|---|---
| Baseline | X | $23.69$ | $25.00$
ERLE | PFB-LMS | $~{}~{}6.27$ | $~{}~{}5.89$ | $~{}~{}4.20$
(dB) | S-U-Net | $25.47$ | $19.29$ | $16.24$
| SR-U-Net | $32.71$ | $42.53$ | $44.31$

PESQ gain for the synthetic testset
---
$0.00$ | $0.23$ | $0.28$ | $0.03$
(Unprocessed) | (PFB-LMS) | (S-U-Net) | (SR-U-Net)

Therefore, we submitted to the ICASSP2021 AEC Challenge the near-end speech
signals inferred by the optimized U-Net architecture obtained in Section II-D
trained only with the synthetic dataset, i.e., the S-U-Net.

###  III-E AEC Challenge Results

Table IV presents the overall subjective scores obtained by our model in the
AEC Challenge [18], together with the particular scores achieved for the
“Clean” and “Noisy” subsets. The blind testset covered four different real
scenarios: Single-talk Near-end (ST NE) where only the near-end signal is
present, Single-talk Far-end (ST FE) Echo where only the echo due to the far-
end signal is present, Double-Talk (DT) Echo where the echo due to the far-end
signal and the near-end signal are present, and Double-Talk (DT) Others where
further impairments have been added to the DT Echo signal conditions. The
subjective score for the ST NE scenario has been the MOS obtained applying the
ITU-T Recommendation P.808 [31]. The subjective score to evaluate the other
three scenarios has been the Degradation Mean Opinion Score (DMOS) obtained by
the application of the ITU-T Recommendation P.831 [32]. The overall score for
each column is calculated as their mean value. Bold numbers indicate a score
above the percentile 30, that is, $30$% of the teams present a lower score. We
ranked 12th among 17 teams, just below the Microsoft baseline. Among the teams
from Academia, we ranked the fifth of ten Universities. From Table IV, it can
be appreciated that our model presents a good behaviour in single-talk and
double-talk scenarios, but it lacks of robustness for non-trained scenarios as
the “ST NE” and “DT Other” sets. Given the limited synthetic dataset used for
training, we are confident that the U-Net performance could be improved by
expanding the training dataset.

TABLE IV: MOS achieved by our model in the AEC Challenge. Scenario | Clean | Noisy | Overall
---|---|---|---
ST NE (MOS) | $\mathbf{3.66}$ | $3.36$ | $3.51$
ST FE Echo (DMOS) | $\mathbf{4.05}$ | $\mathbf{3.52}$ | $\mathbf{3.79}$
DT Echo (DMOS) | $\mathbf{4.08}$ | $\mathbf{3.81}$ | $\mathbf{3.94}$
DT Other (DMOS) | $3.09$ | $3.02$ | $3.06$
Overall | $\mathbf{3.72}$ | $\mathbf{3.43}$ | $\mathbf{3.57}$

##  IV Conclusion

We presented in this work the AEC system used to obtain the estimated near-end
speech signals submitted to the ICASSP 2021 AEC Challenge as the “Universitat
Politècnica de València” team. It uses a U-Net convolutional neural network
whose main hyperparameters have been optimized to obtain the best AEC
performance, but limiting the size of the network in order to meet the latency
restriction of $40$ ms imposed by the challenge’s rules. For the network
training and testing, we have used a synthetic dataset (S-U-Net) and a
synthetic dataset together with the single-talk set of a real dataset (SR-U-
Net), both datasets were provided by the challenge organizer. The S-U-Net
model presented better results for double-talk scenarios and their results
were submitted to the challenge, obtaining an overall MOS of $3.57$ and
ranking 12th among 17 teams.

## Acknowledgment

This work has been partially supported by grants RTI2018-098085-B-C41
(MCIU/AEI/FEDER) and PROMETEO/2019/109. M.F. Dolz work was also supported by
the Plan GenT project CDEIGENT/2018/014 of the _Generalitat Valenciana_ and J.
Silva-Rodríguez work was also supported by the Spanish Government under FPI
Grant [PRE2018-083443].

## References

  * [1] R. Scott, “Must have video conferencing statistics 2020 [Online],” Available at https://www.uctoday.com/collaboration/video-conferencing/video-conferencing-statistics (2020/10/15).
  * [2] M. M. Sondhi, “An adaptive echo canceller,” _The Bell System Technical Journal_ , vol. 46, no. 3, pp. 497–511, 1967.
  * [3] S. Haykin, _Adaptive Filter Theory, 4th edn_. Prentice-Hall, Upper Saddle River, N, 2002.
  * [4] E. Hänsler and G. Schmidt, _Topics in acoustic echo and noise control: selected methods for the cancellation of acoustical echoes, the reduction of background noise, and speech processing_. Springer Science & Business Media, 2006.
  * [5] S. Gustafsson, R. Martin, and P. Vary, “Combined acoustic echo control and noise reduction for hands-free telephony,” _Signal Proc._ , vol. 64, no. 1, pp. 21–32, 1998.
  * [6] F. Kuech and W. Kellermann, “Nonlinear Residual Echo Suppression using a Power Filter Model of the Acoustic Echo Path,” in _2007 IEEE Int. Conf. on Acoustics, Speech and Signal Proc. (ICASSP)_ , vol. 1, 2007, pp. I–73–I–76.
  * [7] J. Sohn, N. Kim, and W. Sung, “A statistical model-based voice activity detection,” _IEEE Signal Proc. Letters_ , vol. 6, no. 1, pp. 1–3, 1999.
  * [8] J. Benesty, D. R. Morgan, and J. H. Cho, “A new class of doubletalk detectors based on cross-correlation,” _IEEE Trans. on Speech and Audio Proc._ , vol. 8, no. 2, pp. 168–172, 2000.
  * [9] M. Iqbal, J. Stokes, J. Platt, A. Surendran, and S. Grant, “DoubleTalk Detection using Real-time Recurrent Learning,” in _2006 Int. Workshop on Acoustic Echo and Noise Control_ , 2006.
  * [10] A. Schwarz, C. Hofmann, and W. Kellermann, “Spectral feature-based nonlinear residual echo suppression,” in _2013 IEEE Workshop on Appl. of Signal Proc. to Audio and Acoustics_ , 2013, pp. 1–4.
  * [11] G. Carbajal, R. Serizel, E. Vincent, and E. Humbert, “Multiple-Input Neural Network-Based Residual Echo Suppression,” in _2018 IEEE Int. Conf. on Acoustics, Speech and Signal Proc. (ICASSP)_ , 2018, pp. 231–235.
  * [12] H. Chen, T. Xiang, K. Chen, and J. Lu, “Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet,” in _Interspeech 2020_ , 2020. [Online]. Available: http://arxiv.org/abs/2005.07631
  * [13] H. Zhang and D. Wang, “Deep Learning for Acoustic Echo Cancellation in Noisy and Double-Talk Scenarios,” in _Interspeech 2018_ , 2018, pp. 3239–3243.
  * [14] H. Zhang, K. Tan, and D. Wang, “Deep Learning for Joint Acoustic Echo and Noise Cancellation with Nonlinear Distortions,” in _Interspeech 2019_ , ISCA, 2019, pp. 4255–4259.
  * [15] A. Fazel, M. El-Khamy, and J. Lee, “Deep Multitask Acoustic Echo Cancellation,” in _Interspeech 2019_ , 2019, pp. 4250–4254.
  * [16] ——, “CAD-AEC: Context-Aware Deep Acoustic Echo Cancellation,” in _2020 IEEE Int. Conf. on Acoustics, Speech and Signal Proc. (ICASSP)_ , 2020, pp. 6919–6923.
  * [17] K. Sridhar, R. Cutler, A. Saabas, T. Parnamaa, H. Gamper, S. Braun, R. Aichner, and S. Srinivasan, “ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets and Testing Framework,” _arXiv preprint arXiv:2009.04972_ , 2020\. [Online]. Available: https://arxiv.org/pdf/2009.04972.pdf
  * [18] R. Cutler, A. Saabas, T. Pärnamaa, K. Sridhar, R. Aichner, S. Braun, H. Gamper, and S. Srinivasan, “Acoustic echo cancellation challenge - ICASSP 2021 [Online],” Available at https://aec-challenge.azurewebsites.net/index.html (2020/10/15).
  * [19] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in _Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015_ , 2015, pp. 234–241.
  * [20] A. Jansson, E. Humphrey, N. Montecchio, R. Bittner, A. Kumar, and T. Weyde, “Singing Voice Separation with Deep U-Net Convolutional Networks,” in _18th Int. Soc. for Music Inf. Retrieval Conf., ISMIR 2017_ , 2017, pp. 745–751.
  * [21] V. Kadandale, J. Montesinos, G. Haro, and E. Gómez, “Multi-task U-Net for Music Source Separation,” in _IEEE International Workshop on Multimedia Signal Proc. (MMSP)_ , 2020, pp. 1–6.
  * [22] Z. Zhang, Q. Liu, and Y. Wang, “Road Extraction by Deep Residual U-Net,” _IEEE Geoscience and Remote Sensing Letters_ , vol. 15, no. 5, pp. 749–753, 2018.
  * [23] A. Kalapahar, J. Silva-Rodríguez, A. Colomer, F. López-Mir, and V. Naranjo, “Gleason Grading of Histology Prostate Images Through Semantic Segmentation via Residual U-Net,” in _2020 IEEE International Conference on Image Processing (ICIP)_ , 2020, pp. 2501–2505.
  * [24] M. Pumperla, “Hyperas: a very simple convenience wrapper around hyperopt for fast prototyping with keras models [Online],” Available at https://github.com/maxpumperla/hyperas (2020/10/20).
  * [25] J. Bergstra, D. Yamins, and D. Cox, “Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms,” in _Proc. 12th Python in science conference_ , vol. 13, 2013, p. 20.
  * [26] L. Bottou, “Stochastic gradient descent tricks,” in _Neural networks: Tricks of the trade_. Springer, 2012, pp. 421–436.
  * [27] A. Tato and R. Nkambou, “Improving Adam Optimizer,” 2018. [Online]. Available: https://openreview.net/forum?id=HJfpZq1DM
  * [28] Y. Xia, S. Braun, C. Reddy, H. Dubey, R. Cutler, and I. Tashev, “Weighted Speech Distortion Losses for Neural-Network-Based Real-Time Speech Enhancement,” in _2020 IEEE Int. Conf. on Acoustics, Speech and Signal Proc. (ICASSP)_ , 2020, pp. 871–875.
  * [29] ITU-T, “Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs,” _ITU-T Recommendation P.862_ , 2001.
  * [30] B. Farhang-Boroujeny, _Adaptive Filters: Theory and Applications_ , 2nd ed. Wiley, 2013.
  * [31] B. Naderi and R. Cutler, “An open source implementation of itu-t recommendation p. 808 with validation,” _arXiv preprint arXiv:2005.08138_ , 2020.
  * [32] ITU-T, “P.831 : Subjective performance evaluation of network echo cancellers,” _ITU-T Recommendation P.831_ , 1998.