Fundamental Frequency Estimation Based on Pitch-Scaled Harmonic Filtering

In this paper, we present an algorithm for robustly estimating the fundamental frequency in speech signals. Our approach is based on pitch-scaled harmonic filtering (PSHF). Following PSHF, we perform a filtering in the frequency domain using the short-time Fourier transform in order to separate the harmonic and non-harmonic parts of the processed signal. We enhance the standard PSHF approach by using a range of window lengths and a cost function that is applied to each window size. This cost function takes into account the energy at the harmonic and non-harmonic frequency coefficients to estimate harmonic energy for a frame. By using energy peaks and applying a cost function that considers the change in pitch in subsequent frames, we then determine the final pitch contour. We evaluated our approach on the Keele database. As the experimental results demonstrate, our methods performs robustly for noisy speech and has a good performance for clean speech in comparison with state-of-the-art algorithms.