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PitchFlower introduces a flow-based neural audio codec that achieves explicit pitch controllability by disentangling fundamental frequency (F0) contours during training through perturbation and conditioning. This approach matches digital signal processing baselines in control accuracy while surpassing them in audio quality, demonstrating robustness against vocoder artifacts inherent in WORLD-transformed data. The framework offers a scalable path for extending deep generative modeling to other speech attributes without compromising fidelity.
PitchFlower: A flow-based neural audio codec with pitch controllability
arXiv:2510.25566v2 Announce Type: replace-cross Abstract: We present PitchFlower, a flow-based neural audio codec with explicit pitch controllability. Our approach promotes pitch disentanglement through a simple perturbation: during training, F0 contours are flattened and randomly shifted at the input, while the true F0 is provided as conditioning to regenerate the original audio. A vector-quantization bottleneck prevents pitch recovery, and a flow-based decoder generates high quality audio. Experiments show that PitchFlower achieves accurate pitch control at the level of DSP baselines but at much higher audio quality, and performs on par with state-of-the-art neural approaches. Notably, despite using WORLD-transformed audio for training, our method filters out the vocoder's inherent artifacts, revealing a strong resilience of deep generative modeling to input degradation. This finding suggests that our framework provides a simple and extensible path that could be extended to other speech attributes.
Abstract:We present PitchFlower, a flow-based neural audio codec with explicit pitch controllability. Our approach promotes pitch disentanglement through a simple perturbation: during training, F0 contours are flattened and randomly shifted at the input, while the true F0 is provided as conditioning to regenerate the original audio. A vector-quantization bottleneck prevents pitch recovery, and a flow-based decoder generates high quality audio. Experiments show that PitchFlower achieves accurate pitch control at the level of DSP baselines but at much higher audio quality, and performs on par with state-of-the-art neural approaches. Notably, despite using WORLD-transformed audio for training, our method filters out the vocoder's inherent artifacts, revealing a strong resilience of deep generative modeling to input degradation. This finding suggests that our framework provides a simple and extensible path that could be extended to other speech attributes.
Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
arXiv:2510.25566 [eess.AS]
(or arXiv:2510.25566v2 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2510.25566 Focus to learn more arXiv-issued DOI via DataCite
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