Statistical Regression Models for Noise Robust F0 Estimation Using Recurrent Deep Neural Networks

Abstract

The fundamental frequency (F0) in a speech signal, which corresponds to pitch, is one of the key features involved in a variety of speech processing tasks. Therefore, accurate F0 estimation has remained an important problem to be solved over decades. However, this problem is difficult, especially in low signal-to-noise ratio (SNR) conditions with unknown noise. In this work, we propose new approaches to noise-robust F0 estimation using recurrent neural networks (RNNs). Recent F0 estimation studies exploit deep neural networks (DNNs), including RNNs, to classify acoustic features into quantized frequency states. In contrast to these classification approaches, we put forward a regression method for F0 tracking, which is accomplished with RNNs. To this end, we propose two variants. Our first model predicts the (scalar) F0 value directly from a spectrum, while our second model predicts a target sinusoidal waveform (with the desired F0) from the raw speech waveform. Our experiments with the pitch tracking database from Graz University of Technology (PTDB-TUG), contaminated by additive noise (NOISEX-92), demonstrate the improvement of the proposed approaches in terms of the gross pitch error (GPE) and fine pitch error (FPE) rates by more than 35% at SNRs between −10 dB and +10 dB against a well-known, noise-robust F0 tracker, PEFAC. Furthermore, our methods outperform state-of-the-art neural network-based approaches by more than 15% in terms of both the FPE and GPE rates over the abovementioned SNR range.