AI-Powered Audio Recognition with KNN and Speech Recognition This project presents an audio recognition system that utilizes the K-Nearest Neighbors (KNN) algorithm and the Speech Recognition API to effectively analyze and identify spoken words or phrases.

Here's how it works:

Audio Processing: The system begins by converting the audio input into a digital signal. Relevant features like pitch and spectral information are then extracted from this signal. KNN Classification: These extracted features act as inputs for the KNN algorithm. The KNN algorithm compares these features to a pre-existing dataset of known audio samples and identifies the closest match, determining the spoken word or phrase. Speech Recognition API Integration: To further enhance accuracy, the Speech Recognition API is incorporated. This API provides additional context and language-specific information, improving the recognition precision. Beyond Recognition:

This system goes beyond simply identifying spoken words. It also generates insightful graphical representations of the audio input:

Audio Signal Visualization: A graph visually depicts the audio signal over time, showcasing the changes in amplitude and frequency. Frequency Band Energy Distribution: A heat graph illustrates how the energy is distributed across different frequency bands within the audio signal. These graphical representations offer valuable insights into the audio data and aid in:

Understanding audio patterns Identifying outliers Recognizing specific audio attributes By combining KNN, Speech Recognition API, and comprehensive visualization, this system not only delivers accurate recognition results but also equips users with a deeper understanding of the audio content.