Model Based Abnormal Acoustic Source Detection Using a Microphone Array

2005 ◽  
pp. 966-969 ◽  
Author(s):  
Heungkyu Lee ◽  
Jounghoon Beh ◽  
June Kim ◽  
Hanseok Ko
Keyword(s):  
Microphone Array ◽  
Source Detection ◽  
Acoustic Source ◽  
Model Based

In-Duct Acoustic Source Detection Using Acoustic Imaging Techniques

2013 ◽  
Author(s):  
Arthur Finez ◽  
Xavier Mettelle ◽  
Christophe Picard ◽  
Romain Leneveu ◽  
Pascal Souchotte
Keyword(s):  
Imaging Techniques ◽  
Acoustic Imaging ◽  
Source Detection ◽  
Acoustic Source

Algorithms for Acoustic Source Orientation Based on Microphone Array in Robot Audition

2013 ◽  
Vol 347-350 ◽  
pp. 922-926
Author(s):  
Ming Wang ◽  
Jian Hui Chen ◽  
Guang Long Wang ◽  
Feng Qi Gao ◽  
Ji Chen Li ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Microphone Array ◽  
Time Difference ◽  
Acoustic Source ◽  
Time Difference Of Arrival ◽  
Orientation Process ◽  
Robot Audition ◽  
Three Stages ◽  
Source Orientation

Acoustic source orientation is an important feature in robot audition. This paper applied a spatial cone six-element (SCSE) microphone array and combined with the time difference of arrival (TDOA) between pairs of spatial separated microphones to estimate acoustic source orientation. Simulate three stages of the acoustic orientation process in a real indoor environment, and results show that the algorithm is simple and effective, reducing the amount of calculation, having anti-noise and reverberation ability to meet the requirements of orientation accuracy.

Model-based Head Orientation Estimation for Smart Devices

2021 ◽  
Vol 5 (3) ◽  
pp. 1-24
Author(s):  
Qiang Yang ◽  
Yuanqing Zheng
Keyword(s):  
Radiation Pattern ◽  
Microphone Array ◽  
Estimation Error ◽  
Microphone Arrays ◽  
Smart Devices ◽  
Head Orientation ◽  
Context Information ◽  
Orientation Estimation ◽  
Model Based ◽  
The Voice

Voice interaction is friendly and convenient for users. Smart devices such as Amazon Echo allow users to interact with them by voice commands and become increasingly popular in our daily life. In recent years, research works focus on using the microphone array built in smart devices to localize the user's position, which adds additional context information to voice commands. In contrast, few works explore the user's head orientation, which also contains useful context information. For example, when a user says, "turn on the light", the head orientation could infer which light the user is referring to. Existing model-based works require a large number of microphone arrays to form an array network, while machine learning-based approaches need laborious data collection and training workload. The high deployment/usage cost of these methods is unfriendly to users. In this paper, we propose HOE, a model-based system that enables Head Orientation Estimation for smart devices with only two microphone arrays, which requires a lower training overhead than previous approaches. HOE first estimates the user's head orientation candidates by measuring the voice energy radiation pattern. Then, the voice frequency radiation pattern is leveraged to obtain the final result. Real-world experiments are conducted, and the results show that HOE can achieve a median estimation error of 23 degrees. To the best of our knowledge, HOE is the first model-based attempt to estimate the head orientation by only two microphone arrays without the arduous data training overhead.

Sensor Planning for Model-Based Acoustic Source Identification

2020 ◽  
Author(s):  
Luke Calkins ◽  
Reza Khodayi-mehr ◽  
Wilkins Aquino ◽  
Michael M. Zavlanos
Keyword(s):  
Source Identification ◽  
Acoustic Source ◽  
Sensor Planning ◽  
Model Based
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