119 research outputs found
Grouping Based Blind Interference Alignment for -user MISO Interference Channels
We propose a blind interference alignment (BIA) through staggered antenna
switching scheme with no ideal channel assumption. Contrary to the ideal
assumption that channels remain constant during BIA symbol extension period,
when the coherence time of the channel is relatively short, channel
coefficients may change during a given symbol extension period. To perform BIA
perfectly with realistic channel assumption, we propose a grouping based
supersymbol structure for -user interference channels which can adjust a
supersymbol length to given coherence time. It is proved that the supersymbol
length could be reduced significantly by an appropriate grouping. Furthermore,
it is also shown that the grouping based supersymbol achieves higher degrees of
freedom than the conventional method with given coherence time.Comment: 5 pages, 3 figures, to appear in IEEE ISIT 201
Retrospective Interference Alignment for Two-Cell Uplink MIMO Cellular Networks with Delayed CSIT
In this paper, we propose a new retrospective interference alignment for
two-cell multiple-input multiple-output (MIMO) interfering multiple access
channels (IMAC) with the delayed channel state information at the transmitters
(CSIT). It is shown that having delayed CSIT can strictly increase the sum-DoF
compared to the case of no CSIT. The key idea is to align multiple interfering
signals from adjacent cells onto a small dimensional subspace over time by
fully exploiting the previously received signals as side information with
outdated CSIT in a distributed manner. Remarkably, we show that the
retrospective interference alignment can achieve the optimal sum-DoF in the
context of two-cell two-user scenario by providing a new outer bound.Comment: 7 pages, 2 figures, to appear in IEEE ICC 201
Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector
The use of deep neural networks (DNN) has dramatically elevated the
performance of automatic speaker verification (ASV) over the last decade.
However, ASV systems can be easily neutralized by spoofing attacks. Therefore,
the Spoofing-Aware Speaker Verification (SASV) challenge is designed and held
to promote development of systems that can perform ASV considering spoofing
attacks by integrating ASV and spoofing countermeasure (CM) systems. In this
paper, we propose two back-end systems: multi-layer perceptron score fusion
model (MSFM) and integrated embedding projector (IEP). The MSFM, score fusion
back-end system, derived SASV score utilizing ASV and CM scores and embeddings.
On the other hand,IEP combines ASV and CM embeddings into SASV embedding and
calculates final SASV score based on the cosine similarity. We effectively
integrated ASV and CM systems through proposed MSFM and IEP and achieved the
SASV equal error rates 0.56%, 1.32% on the official evaluation trials of the
SASV 2022 challenge.Comment: 5 pages, 4 figures, 5 tables, accepted to 2022 Interspeech as a
conference pape
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Consumer Preferences and Willingness to Pay for Advanced Vehicle Technology Options and Fuel Types
At the time of publication J. Shin and C.R. Bhat were at the University of Texas at Ausitn. V.M. Garikapati and D. You at Arizona State University, and R.M. Pendyala at Georgia Institute of Technology.The automotive industry is witnessing a revolution with the advent of advanced vehicular
technologies, smart vehicle options, and fuel alternatives. However, there is very limited research
on consumer preferences for these types of vehicles. But the deployment and penetration of
advanced vehicular technologies in the marketplace, and planning for possible market adoption
scenarios, calls for collection and analysis of consumer preference data related to these emerging
technologies. This study aims to address this gap, offering a detailed analysis of consumer
preference for alternative fuel types and technology options using data collected in choice
experiments conducted on a sample of consumers in South Korea. The results indicate that there
is considerable heterogeneity in consumer preferences for various smart technology options such
as wireless internet, vehicle connectivity, and voice command features, but relatively little
heterogeneity in the preference for smart vehicle applications such as real-time traveler
information on parking and traffic conditions.Civil, Architectural, and Environmental Engineerin
One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification
The application of speech self-supervised learning (SSL) models has achieved
remarkable performance in speaker verification (SV). However, there is a
computational cost hurdle in employing them, which makes development and
deployment difficult. Several studies have simply compressed SSL models through
knowledge distillation (KD) without considering the target task. Consequently,
these methods could not extract SV-tailored features. This paper suggests
One-Step Knowledge Distillation and Fine-Tuning (OS-KDFT), which incorporates
KD and fine-tuning (FT). We optimize a student model for SV during KD training
to avert the distillation of inappropriate information for the SV. OS-KDFT
could downsize Wav2Vec 2.0 based ECAPA-TDNN size by approximately 76.2%, and
reduce the SSL model's inference time by 79% while presenting an EER of 0.98%.
The proposed OS-KDFT is validated across VoxCeleb1 and VoxCeleb2 datasets and
W2V2 and HuBERT SSL models. Experiments are available on our GitHub
Integrated Parameter-Efficient Tuning for General-Purpose Audio Models
The advent of hyper-scale and general-purpose pre-trained models is shifting
the paradigm of building task-specific models for target tasks. In the field of
audio research, task-agnostic pre-trained models with high transferability and
adaptability have achieved state-of-the-art performances through fine-tuning
for downstream tasks. Nevertheless, re-training all the parameters of these
massive models entails an enormous amount of time and cost, along with a huge
carbon footprint. To overcome these limitations, the present study explores and
applies efficient transfer learning methods in the audio domain. We also
propose an integrated parameter-efficient tuning (IPET) framework by
aggregating the embedding prompt (a prompt-based learning approach), and the
adapter (an effective transfer learning method). We demonstrate the efficacy of
the proposed framework using two backbone pre-trained audio models with
different characteristics: the audio spectrogram transformer and wav2vec 2.0.
The proposed IPET framework exhibits remarkable performance compared to
fine-tuning method with fewer trainable parameters in four downstream tasks:
sound event classification, music genre classification, keyword spotting, and
speaker verification. Furthermore, the authors identify and analyze the
shortcomings of the IPET framework, providing lessons and research directions
for parameter efficient tuning in the audio domain.Comment: 5 pages, 3 figures, submit to ICASSP202
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