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• W3031886121 abstract "Outdoor recording is particularly challenging in the presence of wind, which induces highly non-stationary noise in the microphone signals. To enhance a desired signal, e.g., speech, a dedicated noise reduction processing is required. The reduction is usually performed by estimating an unknown set of parameters, e.g., the noise and the speech power spectral densities (PSDs). In contrast to the commonly used assumption of uncorrelated wind noise in multi-channel recordings, we assume the spatial correlation of wind noise contributions to be non-zero at low frequencies when closely-spaced microphones are employed. In our earlier work, we assumed that the spatial coherence was known, i.e., given by a model which depends on the free-field speed and direction of the air stream. As these quantities are unknown in practice, we propose in this work a method to recursively estimate the spatial coherence matrix based on the microphone observations. In addition, we prove the equivalence of two recently developed noise PSD estimation methods when uncorrelated wind noise is assumed, and we propose an approximation of both estimators which is independent of the propagation vector of the speech source at sufficiently low frequency and for a small inter-microphone distance. An evaluation in terms of improvements in speech quality, signal-to-noise ratio and intelligibility is carried out using both simulated and measured wind noise samples." @default.
• W3031886121 created "2020-06-05" @default.
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• W3031886121 date "2020-01-01" @default.
• W3031886121 modified "2023-10-16" @default.
• W3031886121 title "Spatial Coherence-Aware Multi-Channel Wind Noise Reduction" @default.
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• W3031886121 doi "https://doi.org/10.1109/taslp.2020.2998328" @default.
• W3031886121 hasPublicationYear "2020" @default.
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