NDLI: Robust estimation and tracking of pitch period using an efficient Bayesian filter

Content Provider	ACM Digital Library
Author	Soltanian-zadeh, Hamid Gazor, Saeed Amirfattahi, Rassoul Hajimolahoseini, Habib
Copyright Year	2016
Abstract	In this paper, we introduce an algorithm for estimating and tracking the pitch period of audio signals using Bayesian filters. For this purpose, we propose a general Bayesian model, which is robust to the nonstationary 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 nonstationary 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. Most of the existing methods require that the processing window be longer than the largest existing period of the input signal. In contrast, the proposed method does not impose such a limit. 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. Simulation 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.
Starting Page	1219
Ending Page	1229
Page Count	11
File Format	PDF
ISSN	23299290
e-ISSN	23299304
Volume Number	24
Issue Number	7
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian filtering Audio signals Particle filtering Real-time estimation State-space model Time-varying pitch period estimation
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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