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Web/readme.md

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The web version is developed with R Shiny.
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## Screenshots
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### Results of an analysis
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![Homepage and analysis.](images/screenshot1.png)
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### Frequency graph
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![Frequency graph.](images/screenshot2.png)
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### Spectrogram
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![Spectrogram.](images/screenshot3.png)
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## Installation
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1. Install the required Linux libraries with the following command.
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2. If debugging using VSCode, install the [R Debugger](https://github.com/ManuelHentschel/VSCode-R-Debugger) from the Extensions panel.
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3. In VSCode, click the Debug tool and run **Launch Shiny App**. A new browser should display with the web application running.
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## Screenshots
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### Results of an analysis
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> ![Homepage and analysis.](images/screenshot1.png)
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### Frequency graph
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> ![Frequency graph.](images/screenshot2.png)
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### Spectrogram
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> ![Spectrogram.](images/screenshot3.png)

readme.md

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Run the online [demo](https://voicegender.herokuapp.com).
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Read the detailed [article](http://www.primaryobjects.com/2016/06/22/identifying-the-gender-of-a-voice-using-machine-learning/).
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Read the full article [Identifying the Gender of a Voice using Machine Learning](http://www.primaryobjects.com/2016/06/22/identifying-the-gender-of-a-voice-using-machine-learning/).
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This project trains a computer program to identify a voice as male or female, based upon acoustic properties of the voice and speech. The model is trained on a dataset consisting of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R and then processed with artificial intelligence/machine learning algorithms to learn gender-specific traits for classifying the voice as male or female.
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