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The vibrational response of an elastic panel to incident acoustic waves is determined by the direction-of-arrival (DOA) of the waves relative to the spatial structure of the panel's bending modes. By monitoring the relative modal excitations of a panel immersed in a sound field, the DOA of the source may be inferred. In reverberant environments, early acoustic reflections and the late diffuse acoustic field may obscure the DOA of incoming sound waves. Panel microphones may be especially susceptible to the effects of reverberation due to their large surface areas and long-decaying impulse responses. An investigation into the effect of reverberation on the accuracy of DOA estimation with panel microphones was made by recording wake-word utterances in eight spaces with reverberation times (RT60s) ranging from 0.27 to 3.00 s. The responses were used to train neural networks to estimate the DOA. Within ±5°, DOA estimation reliability was measured at 95.00% in the least reverberant space, decreasing to 78.33% in the most reverberant space, suggesting an inverse relationship between RT60 and DOA accuracy. Experimental results suggest that a system for estimating DOA with panel microphones can generalize to new acoustic environments by cross-training the system with data from multiple spaces with different RT60s.
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Estimating Acoustic Direction of Arrival Using a Single Structural Sensor on a Resonant Surface
The direction of arrival (DOA) of an acoustic source is a signal characteristic used by smart audio devices to enable signal enhancement algorithms. Though DOA estimations are traditionally made using a multi-microphone array, we propose that the resonant modes of a surface excited by acoustic waves contain sufficient spatial information that DOA may be estimated using a singular structural vibration sensor. In this work, sensors are affixed to an acrylic panel and used to record acoustic noise signals at various angles of incidence. From these recordings, feature vectors containing the sums of the energies in the panel’s isolated modal regions are extracted and used to train deep neural networks to estimate DOA. Experimental results show that when all 13 of the acrylic panel’s isolated modal bands are utilized, the DOA of incident acoustic waves for a broadband noise signal may be estimated by a single structural sensor to within ±5° with a reliability of 98.4%. The size of the feature set may be reduced by eliminating the resonant modes that do not have strong spatial coupling to the incident acoustic wave. Reducing the feature set to the 7 modal bands that provide the most spatial information produces a reliability of 89.7% for DOA estimates within ±5° using a single sensor.
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- Award ID(s):
- 2104758
- PAR ID:
- 10414802
- Date Published:
- Journal Name:
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP49357.2023.10095986
