Estimating Inharmonic Signals with Optimal Transport Priors

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openAccess

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A4 Artikkeli konferenssijulkaisussa

Date

2023-06-10

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en

Pages

5
1-5

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ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

Abstract

In this work, we consider the problem of estimating the frequency content of inharmonic signals, i.e., sinusoidal mixtures whose components are close to forming a harmonic set. Intuitively, exploiting this closeness should lead to increased estimation performance as compared to unstructured estimation. Earlier approaches to this problem have relied on parametric descriptions of the inharmonicity, stochastic representations, or have resorted to misspecified estimation by ignoring the inharmonicity. Herein, we propose to use a penalized maximum-likelihood framework, where the regularizer is constructed based on optimal mass transport theory, promoting estimates that are close-to-harmonic in a spectral sense. This leads to an estimator that forms a smooth path between the unstructured maximum-likelihood estimator (MLE) and a misspecified MLE (MMLE), as determined by a regularization parameter. In numerical illustrations, we show that the proposed estimator worst-case dominates the MLE and MMLE, thereby allowing for robust estimation for cases when the inharmonicity level is unknown.

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Keywords

Maximum likelihood estimation, Signal processing, Harmonic analysis, Frequency estimation, Acoustics, Speech processing

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Citation

Elvander, F 2023, Estimating Inharmonic Signals with Optimal Transport Priors . in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ., 10095082, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, pp. 1-5, IEEE International Conference on Acoustics, Speech, and Signal Processing, Rhodes Island, Greece, 04/06/2023 . https://doi.org/10.1109/ICASSP49357.2023.10095082