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Scalability and efficiency are desired in neural speech codecs, whichsupports a wide range of bitrates for applications on various devices. Wepropose a collaborative quantization (CQ) scheme to jointly learn the codebookof LPC coefficients and the corresponding residuals. CQ does not simplyshoehorn LPC to a neural network, but bridges the computational capacity ofadvanced neural network models and traditional, yet efficient anddomain-specific digital signal processing methods in an integrated manner. Wedemonstrate that CQ achieves much higher quality than its predecessor at 9 kbpswith even lower model complexity. We also show that CQ can scale up to 24 kbpswhere it outperforms AMR-WB and Opus. As a neural waveform codec, CQ models arewith less than 1 million parameters, significantly less than many othergenerative models.

Efficient And Scalable Neural Residual Waveform Coding With Collaborative Quantization