Maximum A-Posteriori Probability Pitch Tracking in Noisy Environments Using Harmonic Model

Tabrikian, Joseph; Dubnov, Shlomo; Dickalov, Yulya

doi:10.1109/tsa.2003.819950

Cited by 82 publications

(73 citation statements)

References 21 publications

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“…The experimental results in [15] demonstrate the robustness and the accuracy of LMS method relative to standard algorithms such as RAPT [16] and the maximum a posterior estimator (MAP) of [17].…”

Section: Resultsmentioning

confidence: 99%

Determination of pitch range based on onset and offset analysis in modulation frequency domain

Mahmoodzadeh

Abutalebi

Soltanian‐Zadeh

et al. 2010

2010 5th International Symposium on Telecommunications

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Abstract-Auditory scene in a natural environment contains multiple sources. Auditory scene analysis (ASA) is the process in which the auditory system segregates a scene into streams corresponding to different sources. The determination of range of pitch frequency is necessary for segmentation. We propose a system to determine the range of pitch frequency by analyzing onsets and offsets in modulation frequency domain. In the proposed system, first the modulation spectrum of speech is calculated and then, in each subband onsets and offsets will be detected. Thereafter, the segments are generated by matching corresponding onset and offset front. Finally, by choosing the desired segments, the rage of pitch frequency is determined. Systematic evaluation shows that the range of pitch frequency is estimated with good accuracy.

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Section: Resultsmentioning

confidence: 99%

Determination of pitch range based on onset and offset analysis in modulation frequency domain

Mahmoodzadeh

Abutalebi

Soltanian‐Zadeh

et al. 2010

2010 5th International Symposium on Telecommunications

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“…Furthermore, including prior information on how the fundamental frequency evolves from frame to frame enabled a maximum a posteriori (MAP) estimator capable of tracking fundamental frequencies through a dynamic programming implementation (Tabrikian et al, 2004). While some methods of fundamental frequency estimation implicitly provide voicing classification, other methods have been developed explicitly for voicing classification (Dhananjaya and Yegnanarayana, 2013;Harding and Milner, 2012).…”

Section: Yin Takes Peaks Of the Squared Difference Function As Fundammentioning

confidence: 99%

Estimating acoustic speech features in low signal-to-noise ratios using a statistical framework

Harding

Milner

2017

Computer Speech & Language

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“…Instead, it is preferable to use methods that approximate the recursive Bayesian filtering equations directly. These approaches include maximum a posteriori filters [11], particle filters [12,13], and point mass filters (PMFs) [14].…”

Section: Introductionmentioning

confidence: 99%

On frequency tracking in harmonic acoustic signals

Lindfors

Hendeby

Gustafsson

et al. 2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-Acoustic frequency tracking of a harmonic signal with continuously varying frequency is considered. The RaoBlackwellized point mass filter (RBPMF), previously proposed by the authors for mechanical vibration tracking, is applied to the problem. The RBPMF is compared with two periodogram-based methods, and the similarities and differences between them are explained. Both experimental and simulation results in a Doppler frequency tracking scenario are presented, and the results show that the RBPMF can have significantly less estimation error than the competing methods.

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Maximum A-Posteriori Probability Pitch Tracking in Noisy Environments Using Harmonic Model

Cited by 82 publications

References 21 publications

Determination of pitch range based on onset and offset analysis in modulation frequency domain

Determination of pitch range based on onset and offset analysis in modulation frequency domain

Estimating acoustic speech features in low signal-to-noise ratios using a statistical framework

On frequency tracking in harmonic acoustic signals

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