Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings
Abstract Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech conten...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2021-08-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12039 |
| _version_ | 1797423206328434688 |
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| author | Georgios Karantaidis Constantine Kotropoulos |
| author_facet | Georgios Karantaidis Constantine Kotropoulos |
| author_sort | Georgios Karantaidis |
| collection | DOAJ |
| description | Abstract Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech content deteriorates ENF estimation accuracy. Herein, the authors propose a non‐parametric approach for ENF estimation, which incorporates a customised lag window design into the Blackman–Tukey spectral estimation method. Leakage reduction is formulated as a problem of energy maximisation within the main lobe of the spectral window. The proposed approach is compared to state‐of‐the‐art methods for ENF estimation. Maximum correlation coefficient and minimum standard deviation of errors are employed to measure ENF estimation accuracy. Hypothesis testing is performed to determine whether the improvements in ENF estimation accuracy of the proposed approach over the state‐of‐the‐art methods are statistically significant. Experimental results and statistical tests indicate that the proposed approach improves ENF estimation against many state‐of‐the‐art methods. |
| first_indexed | 2024-03-09T07:43:04Z |
| format | Article |
| id | doaj.art-3af641133a7c400496d169977f43207b |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T07:43:04Z |
| publishDate | 2021-08-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-3af641133a7c400496d169977f43207b2023-12-03T04:10:10ZengHindawi-IETIET Signal Processing1751-96751751-96832021-08-0115639640910.1049/sil2.12039Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordingsGeorgios Karantaidis0Constantine Kotropoulos1Department of Informatics Aristotle University of Thessaloniki Thessaloniki GreeceDepartment of Informatics Aristotle University of Thessaloniki Thessaloniki GreeceAbstract Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech content deteriorates ENF estimation accuracy. Herein, the authors propose a non‐parametric approach for ENF estimation, which incorporates a customised lag window design into the Blackman–Tukey spectral estimation method. Leakage reduction is formulated as a problem of energy maximisation within the main lobe of the spectral window. The proposed approach is compared to state‐of‐the‐art methods for ENF estimation. Maximum correlation coefficient and minimum standard deviation of errors are employed to measure ENF estimation accuracy. Hypothesis testing is performed to determine whether the improvements in ENF estimation accuracy of the proposed approach over the state‐of‐the‐art methods are statistically significant. Experimental results and statistical tests indicate that the proposed approach improves ENF estimation against many state‐of‐the‐art methods.https://doi.org/10.1049/sil2.12039correlation methodscorrelatorsspectral analysisspeech processingstatistical analysisstatistical testing |
| spellingShingle | Georgios Karantaidis Constantine Kotropoulos Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings IET Signal Processing correlation methods correlators spectral analysis speech processing statistical analysis statistical testing |
| title | Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings |
| title_full | Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings |
| title_fullStr | Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings |
| title_full_unstemmed | Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings |
| title_short | Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings |
| title_sort | blackman tukey spectral estimation and electric network frequency matching from power mains and speech recordings |
| topic | correlation methods correlators spectral analysis speech processing statistical analysis statistical testing |
| url | https://doi.org/10.1049/sil2.12039 |
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