2,852 research outputs found
Design of Block Transceivers with Decision Feedback Detection
This paper presents a method for jointly designing the transmitter-receiver
pair in a block-by-block communication system that employs (intra-block)
decision feedback detection. We provide closed-form expressions for
transmitter-receiver pairs that simultaneously minimize the arithmetic mean
squared error (MSE) at the decision point (assuming perfect feedback), the
geometric MSE, and the bit error rate of a uniformly bit-loaded system at
moderate-to-high signal-to-noise ratios. Separate expressions apply for the
``zero-forcing'' and ``minimum MSE'' (MMSE) decision feedback structures. In
the MMSE case, the proposed design also maximizes the Gaussian mutual
information and suggests that one can approach the capacity of the block
transmission system using (independent instances of) the same (Gaussian) code
for each element of the block. Our simulation studies indicate that the
proposed transceivers perform significantly better than standard transceivers,
and that they retain their performance advantages in the presence of error
propagation.Comment: 14 pages, 8 figures, to appear in the IEEE Transactions on Signal
Processin
Value Added of Teachers in High-Poverty Schools and Lower-Poverty Schools
This paper examines whether teachers in schools serving students from high-poverty backgrounds are as effective as teachers in schools with more advantaged students. The question is important. Teachers are recognized as the most important school factor affecting student achievement, and the achievement gap between disadvantaged students and their better off peers is large and persistent. Using student-level microdata from 2000-2001 to 2004-2005 from Florida and North Carolina, the authors compare the effectiveness of teachers in high-poverty elementary schools (>70% FRL students) with that of teachers in lower-poverty elementary schools
Coded Speech Quality Measurement by a Non-Intrusive PESQ-DNN
Wideband codecs such as AMR-WB or EVS are widely used in (mobile) speech
communication. Evaluation of coded speech quality is often performed
subjectively by an absolute category rating (ACR) listening test. However, the
ACR test is impractical for online monitoring of speech communication networks.
Perceptual evaluation of speech quality (PESQ) is one of the widely used
metrics instrumentally predicting the results of an ACR test. However, the PESQ
algorithm requires an original reference signal, which is usually unavailable
in network monitoring, thus limiting its applicability. NISQA is a new
non-intrusive neural-network-based speech quality measure, focusing on
super-wideband speech signals. In this work, however, we aim at predicting the
well-known PESQ metric using a non-intrusive PESQ-DNN model. We illustrate the
potential of this model by predicting the PESQ scores of wideband-coded speech
obtained from AMR-WB or EVS codecs operating at different bitrates in noisy,
tandeming, and error-prone transmission conditions. We compare our methods with
the state-of-the-art network topologies of QualityNet, WaweNet, and DNSMOS --
all applied to PESQ prediction -- by measuring the mean absolute error (MAE)
and the linear correlation coefficient (LCC). The proposed PESQ-DNN offers the
best total MAE and LCC of 0.11 and 0.92, respectively, in conditions without
frame loss, and still is best when including frame loss. Note that our model
could be similarly used to non-intrusively predict POLQA or other (intrusive)
metrics. Upon article acceptance, code will be provided at GitHub
Employing Real Training Data for Deep Noise Suppression
Most deep noise suppression (DNS) models are trained with reference-based
losses requiring access to clean speech. However, sometimes an additive
microphone model is insufficient for real-world applications. Accordingly, ways
to use real training data in supervised learning for DNS models promise to
reduce a potential training/inference mismatch. Employing real data for DNS
training requires either generative approaches or a reference-free loss without
access to the corresponding clean speech. In this work, we propose to employ an
end-to-end non-intrusive deep neural network (DNN), named PESQ-DNN, to estimate
perceptual evaluation of speech quality (PESQ) scores of enhanced real data. It
provides a reference-free perceptual loss for employing real data during DNS
training, maximizing the PESQ scores. Furthermore, we use an epoch-wise
alternating training protocol, updating the DNS model on real data, followed by
PESQ-DNN updating on synthetic data. The DNS model trained with the PESQ-DNN
employing real data outperforms all reference methods employing only synthetic
training data. On synthetic test data, our proposed method excels the
Interspeech 2021 DNS Challenge baseline by a significant 0.32 PESQ points. Both
on synthetic and real test data, the proposed method beats the baseline by 0.05
DNSMOS points - although PESQ-DNN optimizes for a different perceptual metric
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