A method to improve the localization of a sound source using a spherical microphone array embedded into autonomous systems is presented. The method is based on a low-complexity diagonal unloading (DU) beamforming in the spherical harmonic (SH) domain using a frequency smoothing power transform (FSPT) of the covariance matrices with a novel ego-noise reduction. The attenuation of the ego-noise in the signal-plus-ego-noise broadband FSTP covariance matrix is achieved by estimating the FSPT ego-noise covariance matrix and exploiting the subspace orthogonality property using a diagonal unloading procedure. Experiments with controlled real-world recordings performed by an aerial drone equipped with a 19-microphone spherical array while sensing a flying target drone demonstrate the efficiency of the proposed method.
Spherical Harmonic Diagonal Unloading Beamforming with Ego-Noise Reduction for DOA Estimation from Autonomous Systems
Salvati D.;Drioli C.;Foresti G. L.
2021-01-01
Abstract
A method to improve the localization of a sound source using a spherical microphone array embedded into autonomous systems is presented. The method is based on a low-complexity diagonal unloading (DU) beamforming in the spherical harmonic (SH) domain using a frequency smoothing power transform (FSPT) of the covariance matrices with a novel ego-noise reduction. The attenuation of the ego-noise in the signal-plus-ego-noise broadband FSTP covariance matrix is achieved by estimating the FSPT ego-noise covariance matrix and exploiting the subspace orthogonality property using a diagonal unloading procedure. Experiments with controlled real-world recordings performed by an aerial drone equipped with a 19-microphone spherical array while sensing a flying target drone demonstrate the efficiency of the proposed method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
