Sunil Sivadas

Bio: Sunil Sivadas is an academic researcher from Institute for Infocomm Research Singapore. The author has contributed to research in topics: Fundamental frequency & Pitch detection algorithm. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Pitch tracking in reverberant environments

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.

A Comparative Study between Pitch Detection Techniques on Reverberant Speech Signals

Abstract: Reverberation is one of the effects that occur regularly in closed room due to multiple reflections. This paper investigates the result of reverberation on both male and female speech signals. This effect is reflected in pitch frequency of speech signals. This parameter is important as it is usually used for speaker identification. Hence, several methods for pitch frequency estimation are investigated and compared on clear and reverberant male and female speech signals to select the one that is not affected so much by the reverberation effect.