Double-Cross-Correlation Processing for Blind Sampling-Rate and Time-Offset Estimation

Chinaev, Aleksej; Thuene, Philipp; Enzner, Gerald

doi:10.1109/taslp.2021.3071967

Cited by 15 publications

(5 citation statements)

References 41 publications

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“…In (8), F min and F max are introduced for numerical stability and avoiding the phase wrapping effect. The WACD method [20] and other CD-based methods [18], [27] can analytically estimate SRO. They are thus suitable for tracking time-varying SRO frame-by-frame [33], [34].…”

Section: B Weighted Average Coherence Drift (Wacd) Methodsmentioning

confidence: 99%

Efficient Joint Optimization of Sampling Rate Offsets Using Entire Multichannel Signal

Masuyama,

Yamaoka,

Kawamura

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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In this paper, we propose a joint estimation method for the sampling rate offsets (SROs) of multiple recording devices. In wireless acoustic sensor networks, distributed microphones are connected to different analog-to-digital converters, and thus SROs occur on non-reference channels, which degrades the performance of various array signal processing techniques. To address this problem, we propose to jointly estimate and compensate SROs of all the non-reference channels. Since the proposed method is formulated as a multivariate non-convex optimization problem, we derive an efficient optimization algorithm on the basis of the majorization-minimization and majorizationequalization algorithms. We further propose to update SROs with only low-frequency components in the initial iterations to avoid undesired local optima. Our experimental results validate the effectiveness of the joint estimation of all SROs and demonstrate its advantage in subsequent array signal processing.

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Section: B Weighted Average Coherence Drift (Wacd) Methodsmentioning

confidence: 99%

Efficient Joint Optimization of Sampling Rate Offsets Using Entire Multichannel Signal

Masuyama,

Yamaoka,

Kawamura

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…For notational ease, we hereafter use τ = τ 21 in this section. Then, the objective function (18) at the kth frequency becomes…”

Section: A Problem Formulationmentioning

confidence: 99%

“…Speech enhancement is also important to extract an desired signal from noisy observation(s) [14]- [16]. Resampling [17], [18] and synchronization [19] are also general topics, especially for asynchronous distributed systems [20]. For these techniques, the TDs are important spatial features, where any improvement in TD estimation (TDE) directly translates to their better performance, and numerous studies exist [1], [21].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Consistent Time Delays in Subsample via Auxiliary-Function-Based Iterative Updates

Yamaoka¹,

Wakabayashi²

2022

Preprint

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In this paper, we propose a new algorithm for the estimation of multiple time delays (TDs). Since a TD is a fundamental spatial cue for sensor array signal processing techniques, many methods for estimating it have been studied. Most of them, including generalized cross correlation (CC)based methods, focus on how to estimate a TD between two sensors. These methods can then be easily adapted for multiple TDs by applying them to every pair of a reference sensor and another one. However, these pairwise methods can use only the partial information obtained by the selected sensors, resulting in inconsistent TD estimates and limited estimation accuracy. In contrast, we propose joint optimization of entire TD parameters, where spatial information obtained from all sensors is taken into account. We also introduce a consistent constraint regarding TD parameters to the observation model. We then consider a multidimensional CC (MCC) as the objective function, which is derived on the basis of maximum likelihood estimation. To maximize the MCC, which is a nonconvex function, we derive the auxiliary function for the MCC and design efficient update rules. We additionally estimate the amplitudes of the transfer functions for supporting the TD estimation, where we maximize the Rayleigh quotient under the non-negative constraint. We experimentally analyze essential features of the proposed method and evaluate its effectiveness in TD estimation. Code will be available at https://github.com/onolab-tmu/AuxTDE.

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“…In the field of acoustic signal processing, spatially distributed microphone arrays and wireless acoustic sensor networks (WASNs) have been widely and actively studied [17]- [19]. TDs are necessary information in resampling and synchronization [20]- [22], which is a general topic in WASNs. For these applications, a TDE method that can estimate multiple TDs with high accuracy is expected.…”

Section: Introductionmentioning

confidence: 99%

Minimum-Spanning-Tree-Based Time Delay Estimation Robust to Outliers

Yamaoka,

Nakashima,

Wakabayashi

et al. 2023

IEEE Access

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In this paper, we present a novel approach to estimating multiple time delays (TDs) in sensor arrays that is robust to outliers of TD measurements. These measurements are typically obtained from the peak of the cross correlation of two sensor signals but may contain outliers due to noise, significantly degrading the performance of downstream applications. To address this issue, we propose an approach to leverage only the best minimum-necessary TD measurements. First, we describe the general properties of TDs and show that the degree of freedom of TDs is the number of sensors minus one for a signal source, which indicates that the full set of TDs is redundant in this case. We then consider selecting nonredundant TDs from all measurements given the necessary and sufficient condition to reconstruct all TDs uniquely. We represent this condition by utilizing the graph theory, and then, formulate an optimization problem to select the optimal nonredundant TDs while satisfying the condition above. We reduce this problem to the problem of finding a minimum spanning tree and propose an efficient algorithm for TD estimation. Experimental evaluation shows that our method successfully eliminates outliers while ensuring that all TDs can be restored.

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Double-Cross-Correlation Processing for Blind Sampling-Rate and Time-Offset Estimation

Cited by 15 publications

References 41 publications

Efficient Joint Optimization of Sampling Rate Offsets Using Entire Multichannel Signal

Efficient Joint Optimization of Sampling Rate Offsets Using Entire Multichannel Signal

Estimation of Consistent Time Delays in Subsample via Auxiliary-Function-Based Iterative Updates

Minimum-Spanning-Tree-Based Time Delay Estimation Robust to Outliers

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