This web application is a real-time spectrogram visualizer that captures and processes audio input from multiple microphones. Users can select one or more available microphones from their device, and the application will merge all selected audio streams before processing them into a single visual representation. The spectrogram displays frequency information in real-time, allowing users to observe the spectral characteristics of sound.
The spectrogram is a visual representation of the frequency content of a sound over time. It is commonly used in audio processing, music analysis, and other applications where understanding the frequency content of sound is important. It is often used in audio engineering, music production, and signal processing to analyze and visualize audio signals.
The application works by utilizing the Web Audio API to access microphone input, process the frequency data through an analyzer, and render it onto a canvas element. Each selected microphone contributes to the combined audio stream, meaning multiple sources are mixed together before the spectrum is visualized. This ensures that users can observe the overall frequency response from multiple inputs simultaneously.
Possible applications include sound analysis, speech visualization, music frequency monitoring, and educational purposes in audio signal processing. This tool provides an intuitive way to examine audio characteristics and is useful for those interested in understanding how different sound sources contribute to a combined frequency spectrum.
While this application focuses on visualizing audio data as a spectrogram, reversing the process—converting a spectrogram back into speech or text—is a complex task that involves advanced techniques in signal processing and machine learning. Reconstructing speech from a spectrogram can be achieved using algorithms like the Griffin-Lim method or neural networks trained for audio reconstruction. However, this process is not perfect, as some information about the original signal (such as phase data) may be lost during the transformation into a spectrogram. Additionally, extracting text from the reconstructed audio would require a separate speech-to-text system. Although technically feasible, implementing this feature would require significant computational resources and highly specialized algorithms, which are beyond the scope of this application in its current form.