Pitch tracking in reverberant environments

TL;DR: This paper compares Neural Network (NN) based approaches such as the Subband Autocorrelation Classifier (SAcC) with signal processing based methods such as YIN and RAPT and shows that multi-style training of NN using the CC+SA cC feature outperforms all the other methods.

Abstract: Pitch, or fundamental frequency, estimation is an important problem in speech processing. Research on pitch extraction is several years old and numerous algorithms have been developed over the years to improve its accuracy. It becomes more difficult in the presence of additive noise and reverberation because noise corrupts the periodicity information which is vital for estimating the pitch. In this paper, we present a quantitative analysis on pitch tracking in the presence of reverberation by different state of the art methods. We compare Neural Network (NN) based approaches such as the Subband Autocorrelation Classifier (SAcC) with signal processing based methods such as YIN and RAPT. We enhance the performance of SAcC by introducing a cross-correlogram feature (CC+SAcC). We further show that multi-style training of NN using the CC+SAcC feature outperforms all the other methods. Experiments were conducted using artificially reverberated Keele and TIMIT databases with room impulse responses of varying T60 values.