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This paper introduces a pitch estimation and tracking system that leverages subband decomposition via an auditory filterbank optimized with a novel frequency coverage metric. The subband signals are encoded using computational auditory scene analysis principles, and normalized autocorrelations are computed for pitch estimation. A generalized labeled multi-Bernoulli filter, incorporating a novel pitch state transition model based on the Ornstein–Uhlenbeck process, is then used for robust pitch tracking.
Optimizing auditory filterbanks with a frequency coverage metric and integrating a generalized labeled multi-Bernoulli filter yields state-of-the-art pitch estimation and tracking, even in noisy and reverberant environments.
This paper proposes a new pitch estimator and a novel pitch tracker for speakers. We first decompose the sound signal into subbands using an auditory filterbank, assuming time–frequency sparsity of human speech. Instead of directly selecting the number of subbands according to experience, we propose a novel frequency coverage metric to derive the number of subbands and the center frequencies of the filterbank. The subband signals are then encoded inspired by the computational auditory scene analysis approach, and the normalized autocorrelations are calculated for pitch estimation. To suppress spurious errors and track the speaker identity, the temporal continuity constraint is exploited and a generalized labeled multi-Bernoulli filter is adapted for pitch tracking, where we use a novel pitch state transition model based on the Ornstein–Uhlenbeck process, and the measurement-driven birth model for adaptive new births of pitch targets. Experimental evaluations with various additive noises demonstrate that the proposed methods have achieved better accuracy compared with several state-of-the-art pitch estimation methods in most studied scenarios. Tests using real recordings in a reverberant room also show that the proposed method is robust against reverberation.