Performance Evaluation of IFAS-based Fund Noisy Environm

In this paper, instantaneous frequency amplitude spectrum (IFAS)-based fundamental frequency estimator is evaluated with speech signal corrupted by additive white gaussian noise. A key idea of the IFAS-based estimator is the use of degree of regularity of periodicity in spectrum of speech signal, defined by a quantity called harmonicity measure, for band selection in the fundamental frequency estimation. Several frequency band and window length selection methods based on harmonicity measure are asessed to find out better performance. It is shown that the performance of the IFAS-based estimator is maintained at constant error rate about 1% from clean speech data up to 15 dB and about 11% at 0 dB SNR. For both female and male speakers, the IFAS-based estimator outperforms several well-known methods particularly at 0 dB SNR.