In this thesis , we introduce an algorithm for estimating and tracking the pitch period of audio signals using Bayesian filters . For this purpose , we propose Bayesian model , which is robust to the non-stationary variations of the amplitude and frequency of the input signal . We also employ a state space model , which uses the delayed versions of the input signal to model the periodicity of non-stationary audio signals . This simple model allows a significant reduction of the required number of particles for the estimation of the pitch period compared to the state-of-the-art particle filtering methods . Moreover , we propose to estimate the logarithm of the period instead of the period itself . We show that the resulting algorithm does not require prior knowledge about the initial state and is robust to the octave error phenomenon , which is a common problem in pitch period estimation methods . Our method often results in a higher time-domain resolution with no perceptible compromise on the frequency-domain resolution especially for high-pitched audio signals such as music . Experimental results reveal that the proposed algorithm outperforms the state-of-the-art pitch period detection algorithms at low signal to noise ratios assuming no prior knowledge about the initial conditions . Key Words Pitch Period Estimation , Bayesian Filtering , Audio Signals , State-Space Model , Real-Time estimation
