Abstract
This paper presents an adaptive peak frequency estimation method using a database that stores PARCOR coefficients as key attributes and the corresponding peak frequencies as nonkey attributes. The least-square lattice algorithm is used to recursively estimate the PARCOR coefficients to adapt to changing circumstances. The nearest neighbor to the current PARCOR coefficient is retrieved from the database, and the corresponding peak frequency is regarded as the estimation. A simultaneous execution of database construction and peak estimation with database update is performed to accelerate the processing time and to improve the estimation accuracy.
Highlights
Estimation of peak frequency of the power spectrum plays an important role in direction-of-arrival estimation, communication system, fault diagnosis, and speech processing
This paper proposes a fast and recursive peak frequency estimation method using a database of partial autocorrelation (PARCOR) coefficients
It is known that the AR spectrum becomes more sensitive to changes in PARCOR coefficient as |ρp| approaches to unity [24, 25]
Summary
Estimation of peak frequency of the power spectrum plays an important role in direction-of-arrival estimation, communication system, fault diagnosis, and speech processing. This paper proposes a fast and recursive peak frequency estimation method using a database of PARCOR coefficients. We estimate the PARCOR coefficients from real speech signals by the LSL algorithm, and compute the corresponding peak frequencies by using either root-finding or peak-picking technique. We estimate the PARCOR coefficients from new observations by the LSL algorithm, retrieve a record with a key nearest to the current key from the database, and use the corresponding peak frequency as the estimation. This estimation method requires neither root finding nor peak picking. We apply the proposed estimation method to real speech signals to evaluate the estimation accuracy, the processing speed, and the storage space
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