Detection of Electric Network Frequency in Audio Recordings–From Theory to Practical Detectors

Hua, Guang; Liao, Han; Wang, Qingyi; Zhang, Haijian; Ye, Dengpan

doi:10.1109/tifs.2020.3009579

Cited by 21 publications

(7 citation statements)

References 31 publications

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“…Given the characteristics of music itself, digital music creation is completely different from traditional music creation. The current digital music creation is based on text, which includes the name of the music, the lyricist, the singer, and the instrument played, and this information is integrated in the computer [8]. The recognition of music based on similar singers' voices or similar styles and rhythms or even similar background music sounds has become a digital music creation method that is gaining attention.…”

Section: Introductionmentioning

confidence: 99%

Digital Music Feature Recognition Based on Wireless Sensing Technology

2022

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With the rapid development of information technology, digital music is subsequently increasing in large quantities, and how a good integration of vocal input and recognition technology can be transformed into digital music can greatly improve the efficiency of music production while ensuring the quality and effect of music. This paper focuses on the implementation and application of human voice input and recognition technology in digital music creation, enabling users to generate digital music forms by simply humming a melodic fragment of a piece of music into a microphone. The paper begins with an introduction to digital music and speech recognition technology and goes on to describe the respective characteristics of various audio formats, which are selected as data sources for digital music creation based on the advantages of the files in terms of retrieval. Following that, the method of extracting musical information from music is described, and the main melody is successfully extracted from the multitrack file to extract the corresponding musical performance information. The feature extraction of humming input melody is further described in detail. The traditional speech recognition method of using short-time energy and short-time overzero rate features for speech endpoint detection is analyzed. Combining the characteristics of humming music, the method of cutting notes by two-stage cutting mode, i.e., combining energy saliency index, overzero rate, and pitch change, is adopted to cut notes, which leads to a substantial improvement in performance. The algorithm uses the melody extraction algorithm to obtain the melody line, merges the short-time segments of the melody line to reduce the error rate of emotion recognition, uses the melody line to segment the music signal to generate segmented segments, then abstracts the features of the segmented segments through a CNN-based structural model, and inputs the output of the model to the regressor in cascade with the melody contour features of the corresponding segmented segments to finally obtain the emotion V / A value of the segmented segments.

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Section: Introductionmentioning

confidence: 99%

Digital Music Feature Recognition Based on Wireless Sensing Technology

2022

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“…To begin with, let us briefly survey ENF estimation in audio recordings, because the same ENF estimation methods are also applied to a one-dimensional (1D) time-series extracted from video recordings. A comprehensive study addressing the ENF detection problem was presented in [4], where many practical detectors were introduced. The detectors were shown to have a reliable performance in relatively short recordings, enabling accurate ENF detection in real-world forensic applications.…”

Section: Introductionmentioning

confidence: 99%

An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings

Karantaidis

Kotropoulos

2021

J. Imaging

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Electric Network Frequency (ENF) is embedded in multimedia recordings if the recordings are captured with a device connected to power mains or placed near the power mains. It is exploited as a tool for multimedia authentication. ENF fluctuates stochastically around its nominal frequency at 50/60 Hz. In indoor environments, luminance variations captured by video recordings can also be exploited for ENF estimation. However, the various textures and different levels of shadow and luminance hinder ENF estimation in static and non-static video, making it a non-trivial problem. To address this problem, a novel automated approach is proposed for ENF estimation in static and non-static digital video recordings. The proposed approach is based on the exploitation of areas with similar characteristics in each video frame. These areas, called superpixels, have a mean intensity that exceeds a specific threshold. The performance of the proposed approach is tested on various videos of real-life scenarios that resemble surveillance from security cameras. These videos are of escalating difficulty and span recordings from static ones to recordings, which exhibit continuous motion. The maximum correlation coefficient is employed to measure the accuracy of ENF estimation against the ground truth signal. Experimental results show that the proposed approach improves ENF estimation against the state-of-the-art, yielding statistically significant accuracy improvements.

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“…a digital audio recorder, connected to a power outlet using a step-down transformer and a voltage divider circuit [4]. The recorded signal is divided into frames and a frequency estimation algorithm is applied to each frame to determine its dominant frequency in the band near to nominal ENF value [5] [6]. The ENF traces present in such power recordings typically has a very high signal-to-noise ratio (SNR), making the extracted ENF signal a high quality reference for the ENF variations.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study on Intra-Grid Location Estimation Using Power ENF Signals

Garg¹,

Hajj-Ahmad²,

Wu³

2021

Preprint

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<div>The Electric Network Frequency (ENF) is a signature of power distribution networks that can be captured by multimedia recordings made in areas where there is electrical activity. This has led to an emergence of several forensic applications based on the use of the ENF signature. Examples of such applications include estimating or verifying the time-of-recording of a media signal and inferring the power grid associated with the location in which the media signal was recorded. In this paper, we carry out a feasibility study to examine the possibility of using embedded ENF traces to pinpoint the location-of-recording of a signal within a power grid. In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces. To this purpose, a high-passed version of an ENF signal is extracted and it is demonstrated that the correlation between two such signals, extracted from recordings made in different geographical locations within the same grid, decreases as the distance between the recording locations increases. We harness this property of correlation in the ENF signals to propose trilateration based localization methods, which pinpoint the unknown location of a recording while using some known recording locations as anchor locations. We also discuss the challenges that need to be overcome in order to extend this work to using ENF traces in noisier audio/video recordings for such fine localization purposes.</div>

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Detection of Electric Network Frequency in Audio Recordings–From Theory to Practical Detectors

Cited by 21 publications

References 31 publications

Digital Music Feature Recognition Based on Wireless Sensing Technology

Digital Music Feature Recognition Based on Wireless Sensing Technology

An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings

Feasibility Study on Intra-Grid Location Estimation Using Power ENF Signals

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