Conference Proceedings
Using AI to get relevant KPIs for Audio Quality
Description
Good metrics for audio quality are rare to find, and some of them doesnt give too much information about what’s the problem, or wether the quality is really one way or another. Some examples are POLQA or ViSQOL, but those two are algorithms requires the reference, and the degraded audio samples to be complete (full reference).
We are proposing two new algorithms based on Deep Learning, and specifically on transformers. The first one is purely focused on speech transcription (SpeechQ) and the second one is about audio classification using audio spectrogram transformers (ASQ-ViT), but using a convolutional neural network as a backbone for feature extraction. The results from this training has lead to a very high correlation with subjective scoring, which proves the effectiveness and simplicity of this algorithm to extract an objective quality measurement from audio outputs.
This talk was presented at Demuxed ’23, a conference for video nerds in San Francisco featuring amazing talks like this one.
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