Fundamental Frequency and Model Order Estimation Using Spatial Filtering

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Resumé

In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment on a trumpet signal show the applicability on real signals.
OriginalsprogEngelsk
Titel2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)
Antal sider5
ForlagIEEE
Publikationsdato2014
Sider5964 - 5968
ISBN (Trykt)9781479928941
DOI
StatusUdgivet - 2014
Begivenhed2014 IEEE International Conference on Acoustics, Speech and Signal Processing - Firenze, Italien
Varighed: 4 maj 20149 maj 2014
Konferencens nummer: 18874

Konference

Konference2014 IEEE International Conference on Acoustics, Speech and Signal Processing
Nummer18874
LandItalien
ByFirenze
Periode04/05/201409/05/2014

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Beamforming
Signal processing
Experiments

Citer dette

Karimian-Azari, S., Jensen, J. R., & Christensen, M. G. (2014). Fundamental Frequency and Model Order Estimation Using Spatial Filtering. I 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) (s. 5964 - 5968). IEEE. https://doi.org/10.1109/ICASSP.2014.6854748
Karimian-Azari, Sam ; Jensen, Jesper Rindom ; Christensen, Mads Græsbøll. / Fundamental Frequency and Model Order Estimation Using Spatial Filtering. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) . IEEE, 2014. s. 5964 - 5968
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title = "Fundamental Frequency and Model Order Estimation Using Spatial Filtering",
abstract = "In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment on a trumpet signal show the applicability on real signals.",
keywords = "Harmonic signal, pitch estimation, model order estimation, microphone arrays, frequency-domain beamforming",
author = "Sam Karimian-Azari and Jensen, {Jesper Rindom} and Christensen, {Mads Gr{\ae}sb{\o}ll}",
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Karimian-Azari, S, Jensen, JR & Christensen, MG 2014, Fundamental Frequency and Model Order Estimation Using Spatial Filtering. i 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) . IEEE, s. 5964 - 5968, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing , Firenze, Italien, 04/05/2014. https://doi.org/10.1109/ICASSP.2014.6854748

Fundamental Frequency and Model Order Estimation Using Spatial Filtering. / Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll.

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) . IEEE, 2014. s. 5964 - 5968.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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N2 - In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment on a trumpet signal show the applicability on real signals.

AB - In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment on a trumpet signal show the applicability on real signals.

KW - Harmonic signal

KW - pitch estimation

KW - model order estimation

KW - microphone arrays

KW - frequency-domain beamforming

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Karimian-Azari S, Jensen JR, Christensen MG. Fundamental Frequency and Model Order Estimation Using Spatial Filtering. I 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) . IEEE. 2014. s. 5964 - 5968 https://doi.org/10.1109/ICASSP.2014.6854748