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RoV: receiving files from voice calls using dual-tone multi-frequency method

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Abstract

When data service is not available, it is almost impossible for smartphones to receive data files. In this paper, we propose a system, called RoV, which can send data files to a smartphone via voice calls instead of data service. In this system, data files are encoded into acoustic signals by the sender, transmitted to the receiver through the normal voice channel, and decoded by the receiver at the end. There are two main challenges for the system: (1) the speaker of a smartphone is usually not accessible during a phone call, making it difficult to directly use a smartphone as the sender; and (2) the voice channel is frequency sensitive, and simple modulation methods do not work well. For the first challenge, RoV simulates a Bluetooth headset to inject voice signals to the voice channel without rooting the smartphone. For the second challenge, RoV utilizes the dual-tone multi-frequency (DTMF) method for encoding and decoding in a suitable frequency range. In addition, we introduce new break symbols in DTMF, which can avoid the synchronization problem and also lead to a simple decoding algorithm. We implement RoV and evaluate its performance with experiments.

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Acknowledgements

The authors would like to thank the reviewers for their constructive comments. This work was supported by the National Natural Science Foundation of China (61972199).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Xiaojun Zhu & Yu Wang

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Xiaojun Zhu

  3. School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, USA

    Zhengrui Qin

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  1. Xiaojun Zhu
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  2. Yu Wang
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  3. Zhengrui Qin
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Correspondence to Xiaojun Zhu.

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Zhu, X., Wang, Y. & Qin, Z. RoV: receiving files from voice calls using dual-tone multi-frequency method. J Supercomput 78, 7304–7320 (2022). https://doi.org/10.1007/s11227-021-04168-7

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  • DOI: https://doi.org/10.1007/s11227-021-04168-7

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