ASQ-ViT (Audio Spectrogram Quality with Visual Transformer)

ASQ-ViT (Audio Spectrogram Quality with Visual Transformer) is an AI-powered tool designed to automatically assess the quality of audio signals. It works by converting sound waves into a visual map of frequencies and amplitude called a spectrogram . The tool then analyzes this spectrogram using a Vision Transformer (ViT), an AI model originally created for image analysis. By treating the audio's visual representation like a picture, ASQ-ViT can effectively evaluate sound quality, providing a score that closely matches how a human would perceive it. A key benefit is that it can do this without needing a "perfect" reference audio sample for comparison.

How it works

The process involves two main steps:

1. Spectrogram Conversion: The audio signal (e.g., a voice recording) is transformed into a 2D image, where the x-axis represents time, the y-axis represents frequency, and the color or intensity of the pixels shows the amplitude of the sound.
2. AI Analysis: The Vision Transformer model analyzes this spectrogram "image." The ViT breaks the image into patches, then uses a self-attention mechanism to identify and evaluate patterns, such as noise, dropouts, or distortions, to generate a final quality score.

Example: A company that provides a voice-activated login feature for its mobile app wants to ensure a high-quality user experience. Instead of manually listening to thousands of recordings, the quality assurance team uses ASQ-ViT to automatically evaluate the audio quality of the login voice prompts. The tool analyzes each user's voice input, looking for issues like a weak signal, background noise, or a crackling sound. It provides a numerical quality score for each login attempt, allowing the company to automatically flag and investigate any audio that falls below an acceptable threshold without needing to compare it to a perfect, noise-free recording.