Audio Decoding by Inverse Problem Solving
This work addresses audio decoding for perceptual codecs, offering incremental improvements in efficiency and performance over legacy methods.
The paper tackles audio decoding as an inverse problem using diffusion posterior sampling, achieving significant improvements in decoding quality for various content types and bitrates, such as enhanced piano performance while maintaining speech quality with a joint model.
We consider audio decoding as an inverse problem and solve it through diffusion posterior sampling. Explicit conditioning functions are developed for input signal measurements provided by an example of a transform domain perceptual audio codec. Viability is demonstrated by evaluating arbitrary pairings of a set of bitrates and task-agnostic prior models. For instance, we observe significant improvements on piano while maintaining speech performance when a speech model is replaced by a joint model trained on both speech and piano. With a more general music model, improved decoding compared to legacy methods is obtained for a broad range of content types and bitrates. The noisy mean model, underlying the proposed derivation of conditioning, enables a significant reduction of gradient evaluations for diffusion posterior sampling, compared to methods based on Tweedie's mean. Combining Tweedie's mean with our conditioning functions improves the objective performance. An audio demo is available at https://dpscodec-demo.github.io/.