Welcome to the ADHD_recognition repository, where we explore the fascinating world of ADHD recognition using personal voice data. This project combines advanced technologies such as Flask, HTML/CSS/JavaScript, librosa, matplotlib, NumPy, OpenSMILE, pandas, Python, scikit-learn, and seaborn to create a comprehensive solution for recognizing ADHD through voice analysis.
Repository Topics: flask, html-css-javascript, librosa, matplotlib, numpy, opensmile, pandas, python, scikit-learn, seaborn
The ADHD_recognition project focuses on leveraging voice data to recognize Attention Deficit Hyperactivity Disorder (ADHD) in individuals. By analyzing various features extracted from personal voice recordings, the project aims to provide insights into the potential presence of ADHD in individuals. This analysis is facilitated through a combination of signal processing techniques, machine learning algorithms, and web development technologies.
For accessing the latest releases of the ADHD_recognition project, please visit the following link:
If you encounter any issues with the provided link, kindly refer to the "Releases" section within the repository for alternative download options or additional information.
Flask is used to build the web application interface for interacting with the ADHD recognition system. It enables seamless integration of backend functionalities with the frontend user interface.
These technologies are utilized to create visually appealing and interactive web pages that facilitate user input and data visualization within the application.
librosa provides essential tools for audio signal processing and feature extraction necessary for analyzing voice recordings in the context of ADHD recognition.
matplotlib is employed for generating various data visualizations, such as spectrograms and feature plots, to aid in the interpretation of voice data analysis results.
NumPy is utilized for efficient numerical computations and array operations essential for handling voice data features and machine learning algorithms.
OpenSMILE offers a comprehensive toolkit for extracting a wide range of audio features from voice recordings, allowing for detailed analysis and modeling of ADHD-related patterns.
pandas is used for data manipulation and analysis, enabling structured handling of voice data features and facilitating exploratory data analysis.
Python serves as the primary programming language for implementing the voice data analysis algorithms, machine learning models, and web application functionalities.
scikit-learn provides a powerful library of machine learning algorithms and tools essential for building predictive models based on voice data features for ADHD recognition.
seaborn is utilized for creating visually appealing data visualizations and statistical plots, enhancing the interpretability of analysis results and model performance.
To utilize the ADHD_recognition system, follow these steps:
- Download the necessary project files from the provided link.
- Install the required dependencies by following the setup instructions in the repository.
- Execute the system on your local machine to start analyzing voice data for ADHD recognition.
In conclusion, the ADHD_recognition project represents a cutting-edge approach to leveraging personal voice data for the recognition of ADHD in individuals. By combining advanced technologies and methodologies, the project aims to enhance our understanding of ADHD symptoms and support diagnostic efforts through non-invasive voice analysis techniques. Visit the project releases to explore the latest advancements in ADHD recognition with personal voice data.
Stay tuned for future updates and enhancements to the ADHD_recognition project by checking the repository regularly. Your feedback and contributions are valuable in shaping the evolution of this innovative solution for ADHD recognition through personal voice analysis.