Short-Range Audio Channels Security: Survey of Mechanisms, Applications, and Research Challenges

Caprolu, Maurantonio; Sciancalepore, Savio; Pietro, Roberto Di

doi:10.1109/comst.2020.2969030

Cited by 10 publications

(3 citation statements)

References 119 publications

(144 reference statements)

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“…While BLE is the de facto choice for all contact tracing solutions in the literature, we believe that more research is needed on different communication technologies. In particular, Ultra-wideband (UWB) carriers as well as acoustic channels and ultrasonic sound waves [15] could be employed to improve the accuracy, privacy, and reliability of proximity tracing.…”

Section: Technology Considerationsmentioning

confidence: 99%

IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-enabled Architecture for Contact Tracing

Tedeschi,

Bakiras,

Di Pietro

2020

Preprint

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Contact tracing promises to help fight the diffusion of COVID-19 via an early detection of possible sources of further contagion. A number of applications have been proposed to this aim. All of them have in common the underlying architecture: smartphones transmit their own beacons, while at the same time storing the beacons sent by other devices-with a reconciliation procedure applied later on. On top of this, solutions are adopted to provide some privacy guarantees to the users. In this paper, we relax the smartphone-centric assumption, and propose an IoTenabled architecture for contact tracing that enjoys the following features: i) it reduces the overhead on the end-user to the bare minimum-it requires the mobile phone to only broadcast its beacons; ii) it provides the user with a degree of privacy not achieved by competing solutions-even in the most privacy adverse scenario, the solution provides k-anonymity; and, iii) it is tunable: the very same architecture can be configured to support several models, ranging from the fully decentralized to the fully centralized ones. Finally, we also highlight open issues and future directions. We believe that the novelty of the proposal, as well as its striking properties and flexibility, has the potential to pave the way for further research.

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Section: Technology Considerationsmentioning

confidence: 99%

IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-enabled Architecture for Contact Tracing

Tedeschi,

Bakiras,

Di Pietro

2020

Preprint

Self Cite

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“…One of the most diffused metrics to analyze a generic sound is the pitch. The pitch of a sound is defined as the fundamental frequency of an audio signal [38]. For instance, in the context of speech analysis, the pitch is often used to discriminate male from female voices, or to identify the specific speaker.…”

Section: Sound Components Analysismentioning

confidence: 99%

Noise2Weight: On Detecting Payload Weight from Drones Acoustic Emissions

Ibrahim¹,

Sciancalepore²,

Pietro³

2020

Preprint

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The increasing popularity of autonomous and remotely-piloted drones have paved the way for several use-cases, e.g., merchandise delivery and surveillance. In many scenarios, estimating with zero-touch the weight of the payload carried by a drone before its physical approach could be attractive, e.g., to provide an early tampering detection.In this paper, we investigate the possibility to remotely detect the weight of the payload carried by a commercial drone by analyzing its acoustic fingerprint. We characterize the difference in the thrust needed by the drone to carry different payloads, resulting in significant variations of the related acoustic fingerprint. We applied the above findings to different usecases, characterized by different computational capabilities of the detection system. Results are striking: using the Mel-Frequency Cepstral Coefficients (MFCC) components of the audio signal and different Support Vector Machine (SVM) classifiers, we achieved a minimum classification accuracy of 98% in the detection of the specific payload class carried by the drone, using an acquisition time of 0.25 s-performances improve when using longer time acquisitions.All the data used for our analysis have been released as opensource, to enable the community to validate our findings and use such data as a ready-to-use basis for further investigations.

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“…Its purpose is to enable computers or other hardware devices to make natural sounds like people. In this environment, to make mobile phones, computers, and other intelligent terminal devices be completely like people that can "speak" and "listen," can understand the natural language of humans, and can get some feedback or according to the instructions to complete the corresponding operation is one of the goals of the current scientific artificial intelligence field [3]. However, for the current TTS (Text-To-Speech) system, it is the main research direction of many enterprises and universities to make it produce clear, understandable, fluent, and natural voices in different scenes to better meet the personalized needs of users.…”

Section: Introductionmentioning

confidence: 99%

Design of an Automatic English Pronunciation Error Correction System Based on Radio Magnetic Pronunciation Recording Devices

Zhang

2021

Journal of Sensors

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In this paper, a system for automatic detection and correction of mispronunciation of native Chinese learners of English by speech recognition technology is designed with the help of radiomagnetic pronunciation recording devices and computer-aided software. This paper extends the standard pronunciation dictionary by predicting the phoneme confusion rules in the language learner’s pronunciation that may lead to mispronunciation and generates an extended pronunciation dictionary containing the standard pronunciation of each word and the possible mispronunciation variations, and automatic speech recognition uses the extended pronunciation dictionary to detect and diagnose the learner’s mispronunciation of phonemes and provides real-time feedback. It is generated by systematic crosslinguistic phonological comparative analysis of the differences in phoneme pronunciation with each other, and a data-driven approach is used to do automatic phoneme recognition of learner speech and analyze the mapping relationship between the resulting mispronunciation and the corresponding standard pronunciation to automatically generate additional phoneme confusion rules. In this paper, we investigate various aspects of several issues related to the automatic correction of English pronunciation errors based on radiomagnetic pronunciation recording devices; design the general block diagram of the system, etc.; and discuss some key techniques and issues, including endpoint detection, feature extraction, and the system’s study of pronunciation standard algorithms, analyzing their respective characteristics. Finally, we design and implement a model of an automatic English pronunciation error correction system based on a radiomagnetic pronunciation recording device. Based on the characteristics of English pronunciation, the correction algorithm implemented in this system uses the similarity and pronunciation duration ratings based on the log posterior probability, which combines the scores of both, and standardizes this system scoring through linear mapping. This system can achieve the purpose of automatic recognition of English mispronunciation correction and, at the same time, improve the user’s spoken English pronunciation to a certain extent.

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Short-Range Audio Channels Security: Survey of Mechanisms, Applications, and Research Challenges

Cited by 10 publications

References 119 publications

IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-enabled Architecture for Contact Tracing

IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-enabled Architecture for Contact Tracing

Noise2Weight: On Detecting Payload Weight from Drones Acoustic Emissions

Design of an Automatic English Pronunciation Error Correction System Based on Radio Magnetic Pronunciation Recording Devices

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