18,720 research outputs found
Synthetic speech detection and audio steganography in VoIP scenarios
The distinction between synthetic and human voice uses the techniques of the current biometric voice recognition systems, which prevent that a person’s voice, no matter if with good or bad intentions, can be confused with someone else’s. Steganography gives the possibility to hide in a file without a particular value (usually audio, video or image files) a hidden message in such a way as to not rise suspicion to any external observer. This article suggests two methods, applicable in a VoIP hypothetical scenario, which allow us to distinguish a synthetic speech from a human voice, and to insert within the Comfort Noise a text message generated in the pauses of a voice conversation. The first method takes up the studies already carried out for the Modulation Features related to the temporal analysis of the speech signals, while the second one proposes a technique that derives from the Direct Sequence Spread Spectrum, which consists in distributing the signal energy to hide on a wider band transmission.
Due to space limits, this paper is only an extended abstract. The full version will contain further details on our research
MPEG-1 bitstreams processing for audio content analysis
In this paper, we present the MPEG-1 Audio bitstreams processing work which our research group is involved in. This work is primarily based on the processing of the encoded bitstream, and the extraction of useful audio features for the purposes of analysis and browsing. In order to prepare for the discussion of these features, the MPEG-1 audio bitstream format is first described. The Application Interface Protocol (API) which we have been developing in C++ is then introduced, before completing the paper with a discussion on audio feature extraction
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
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