DATASET

Audio recordings microphones dataset (gun shot)

Collection: MOBILEMIKES : Data recording from microphones of mobile phones stimulated by specific tones 

Description

Microphones recordings from the built-in microphone from 34 mobile phones (each equipped with a single microphone) stimulated by a gun shot sound. The recordings are in PCM format at 44.1 KHz. No SIM was inserted in the mobile phone and no personal data was recorded. The recording took place in a laboratory. The data set is composed by 800 repetitions of the recording based on the repetition of the gun shot sound for each microphone. The dataset is useful for studies on multi-factor authentication of mobile phones and fight against distribution of counterfeit electronic products.

The data set is a three dimensional matrix with three dimensions: the first is the identifier of the phone, the second is the audio recording and the third is the repetition of the sound.

Contact

Email
Gianmarco.BALDINI (at) ec.europa.eu

Contributors

How to cite

European Commission, Joint Research Centre (JRC) (2020): Audio recordings microphones dataset (gun shot). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/011799a6-80c1-4416-ae15-17116e2fc32f

Data access

Audio recordings microphones dataset (gun shot
Download 
An Evaluation of Entropy Measures for Microphone Identification
Download 

Publications

Publication
An Evaluation of Entropy Measures for Microphone Identification
Baldini, G. and Amerini, I., An Evaluation of Entropy Measures for Microphone Identification, ENTROPY, ISSN 1099-4300 (online), 22 (11), 2020, p. 1235, JRC121576.
  • MDPI, BASEL, SWITZERLAND
  • Abstract

    Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori.

    There is usually a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it ranging from the application of handcrafted statistical features to the recent application of deep learning techniques.

    This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental data set of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.

Additional information

Published by
European Commission, Joint Research Centre
Created date
2020-11-18
Modified date
2022-01-21
Issued date
2020-09-18
Data theme(s)
Science and technology
Update frequency
unknown
Identifier
http://data.europa.eu/89h/011799a6-80c1-4416-ae15-17116e2fc32f
Popularity