Noise-Robust DSP-Assisted Neural Pitch Estimation with Very Low Complexity

Krishna Subramani; Jean-Marc Valin; Jan Buethe; Paris Smaragdis; Mike Goodwin

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arXiv · 2309.14507

Noise-Robust DSP-Assisted Neural Pitch Estimation with Very Low Complexity

Krishna Subramani·Jean-Marc Valin·Jan Buethe·Paris Smaragdis·Mike Goodwin

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Abstract

Pitch estimation is an essential step of many speech processing algorithms, including speech coding, synthesis, and enhancement. Recently, pitch estimators based on deep neural networks (DNNs) have have been outperforming well-established DSP-based techniques. Unfortunately, these new estimators can be impractical to deploy in real-time systems, both because of their relatively high complexity, and the fact that some require significant lookahead. We show that a hybrid estimator using a small deep neural network (DNN) with traditional DSP-based features can match or exceed the performance of pure DNN-based models, with a complexity and algorithmic delay comparable to traditional DSP-based algorithms. We further demonstrate that this hybrid approach can provide benefits for a neural vocoding task.

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Krishna Subramani, Jean-Marc Valin, Jan Buethe, Paris Smaragdis, Mike Goodwin. 2023-09-25. Noise-Robust DSP-Assisted Neural Pitch Estimation with Very Low Complexity. https://arxiv.org/abs/2309.14507

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