15,735 research outputs found
FPGA Implementation of Spectral Subtraction for In-Car Speech Enhancement and Recognition
The use of speech recognition in noisy environments requires the use of speech enhancement algorithms in order to improve recognition performance. Deploying these enhancement techniques requires significant engineering to ensure algorithms are realisable in electronic hardware. This paper describes the design decisions and process to port the popular spectral subtraction algorithm to a Virtex-4 field-programmable gate array (FPGA) device. Resource analysis shows the final design uses only 13% of the total available FPGA resources. Waveforms and spectrograms presented support the validity of the proposed FPGA design
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Final report on the evaluation of RRM/CRRM algorithms
Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin
Scaling Speech Enhancement in Unseen Environments with Noise Embeddings
We address the problem of speech enhancement generalisation to unseen
environments by performing two manipulations. First, we embed an additional
recording from the environment alone, and use this embedding to alter
activations in the main enhancement subnetwork. Second, we scale the number of
noise environments present at training time to 16,784 different environments.
Experiment results show that both manipulations reduce word error rates of a
pretrained speech recognition system and improve enhancement quality according
to a number of performance measures. Specifically, our best model reduces the
word error rate from 34.04% on noisy speech to 15.46% on the enhanced speech.
Enhanced audio samples can be found in
https://speechenhancement.page.link/samples
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
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