> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/ai-product/electronic-network-frequency-analysis.md). # Electronic Network Frequency Analysis Audio can be checked against the power grid, because every recording near mains power carries its hum. The page first defines electrical network frequency (ENF) analysis and then points to a Python tool that extracts it. The same notes are in [Digital Signal Processing (DSP)](/predictive-ml/digital-signal-processing-dsp.md) and [Fourier Transform](/predictive-ml/fourier-transform.md). Electrical network frequency (ENF) analysis is an [audio forensics](https://en.wikipedia.org/wiki/Audio_forensics) technique for validating [audio recordings](https://en.wikipedia.org/wiki/Audio_recording) by comparing frequency changes in background [mains hum](https://en.wikipedia.org/wiki/Mains_hum) in the recording with long-term high-precision historical records of [mains frequency](https://en.wikipedia.org/wiki/Mains_frequency) changes from a database. Each of those four terms links to its Wikipedia article; mains frequency is covered there under utility frequency, and audio recording under sound recording and reproduction. To try it on real files, the first step is extracting the ENF signal from the recording. [pyenf](https://github.com/deerajnagothu/pyenf_extraction) — Python-based ENF extraction from video and audio recordings. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/ai-product/electronic-network-frequency-analysis.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.