I would think that by comparison to image models synthetic data would be relatively easy to generate for audio model training. I’m curious then why it continues to be so difficult to build a nearly flawless audio separation model. Is synthetic data being widely used? Is it just too hard of a problem to train even with this data? I don’t have a good sense of what the most challenging aspects are of audio models.
Unlike images, audio signals are time-dependent and have complex temporal dynamics, making it more challenging to generate realistic synthetic data that captures the nuances of real-world audio. Meanwhile, the complex nature of audio signals, the scarcity of high-quality training data, and the subjective evaluation of audio quality collectively contribute to the ongoing challenges in building near-flawless audio separation models.
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I would think that by comparison to image models synthetic data would be relatively easy to generate for audio model training. I’m curious then why it continues to be so difficult to build a nearly flawless audio separation model. Is synthetic data being widely used? Is it just too hard of a problem to train even with this data? I don’t have a good sense of what the most challenging aspects are of audio models.
Unlike images, audio signals are time-dependent and have complex temporal dynamics, making it more challenging to generate realistic synthetic data that captures the nuances of real-world audio. Meanwhile, the complex nature of audio signals, the scarcity of high-quality training data, and the subjective evaluation of audio quality collectively contribute to the ongoing challenges in building near-flawless audio separation models.