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• W2134025364 abstract "Modern speech processing applications require operation on signal of interest that is contaminated by high level of noise. This situation calls for a greater robustness in estimation of the speech parameters, a task which is hard to achieve using standard speech models. In this paper, we present an optimal estimation procedure for sound signals (such as speech) that are modeled by harmonic sources. The harmonic model achieves more robust and accurate estimation of voiced speech parameters. Using maximum a posteriori probability framework, successful tracking of pitch parameters is possible in ultra low signal to noise conditions (as low as -15 dB). The performance of the method is evaluated using the Keele pitch detection database with realistic background noise. The results show best performance in comparison to other state-of-the-art pitch detectors. Application of the proposed algorithm in a simple speaker identification system shows significant improvement in the performance." @default.
• W2134025364 created "2016-06-24" @default.
• W2134025364 creator A5015780969 @default.
• W2134025364 creator A5042278809 @default.
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• W2134025364 date "2004-01-01" @default.
• W2134025364 modified "2023-09-23" @default.
• W2134025364 title "Maximum A-Posteriori Probability Pitch Tracking in Noisy Environments Using Harmonic Model" @default.
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• W2134025364 doi "https://doi.org/10.1109/tsa.2003.819950" @default.
• W2134025364 hasPublicationYear "2004" @default.
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