Recording time identification method

Abstract

The invention provides a recording time identification method. The recording time identification method includes voice signal preprocessing, power-grid frequency extraction, normalization, design of a correlator and parallel matching algorithm, matching-signal median filtering and output of matching results. The recording time identification method includes firstly extracting power-grid frequency signals in audio frequency to be matched, dividing the power-grid frequency signals into three channels to be matched with frames in power-grid frequency in power lines in parallel so as to obtain correlation degree of the power-grid frequency signals and the power-grid frequency, comparing the correlation degree with the threshold value, judging the frames with the maximum correlation degree, and finally acquiring recording time of audio frequency signals. The recording time identification method adopts a parallel mechanism, matching efficiency is improved. Meanwhile, the recording time identification method is more accurate in recording time identification and is of great significance in audio frequency forensics.

Description

technical field [0001] The invention relates to the field of identification of recording evidence, in particular to a recording time identification method. Background technique [0002] In April 2002, the "Several Provisions of the Supreme People's Court Concerning Evidence in Civil Procedures" stipulated that recordings obtained by legal means can be submitted to the court as evidence. However, often due to the lack of audio forensics technology in the courtroom, the obtained recordings are often insufficient in probative force. The so-called audio forensics is to determine who said what, when, where, and what equipment was used to record the given recording signal, and whether the recording signal has been tampered with and where the tampering occurred. [0003] In 2009, R.c.Mahe discussed the research progress of speech forensics technology. In the same year, J.ECampbell et al. analyzed and discussed forensic speaker recognition. Grigora and E.B.Brixen believe that the ...

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Problems solved by technology

However, the fluctuation range of power grid frequency in my country is large, and it is easily affected by noise and other interference factors. In addition, my country does not have a huge power grid frequency database like foreign countries, which makes the research of voice forensics more difficult.

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