Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  develop a system for microphone selflocalization based on ambient sound, without any assumptions on the 3D locations of the microphones and sound sources.  aim at developing a system capable of dealing with multiple moving sound sources.  will show that this is possible given that there are instances where there are only one dominating sound source. In the first step of the system  employ a feature detection and matching strategy. This produces TDOA data, possibly with missing data and with outliers. Then  use a robust and stratified approach for the parameter estimation.  use robust techniques to calculate initial estimates on the offsets parameters, follod by nonlinear optimization based on a rank criterion. Sequentially  use robust methods for calculating initial estimates of the sound source positions and microphone positions, follod by non-linear Maximum Likelihood estimation of all parameters. The methods are tested and verified using anechoic chamber sound recordings. (Less)
['Zhayida Simayijiang', 'Fredrik Andersson', 'Yubin Kuang', 'Kalle Åström']
I am writing a research paper on a idea AN AUTOMATIC SYSTEM FOR MICROPHONE SELF-LOCALIZATION USING AMBIENT SOUND
[{"from": "human", "value": "I am writing a research paper on a idea AN AUTOMATIC SYSTEM FOR MICROPHONE SELF-LOCALIZATION USING AMBIENT SOUND"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  develop a system for microphone selflocalization based on ambient sound, without any assumptions on the 3D locations of the microphones and sound sources.  aim at developing a system capable of dealing with multiple moving sound sources.  will show that this is possible given that there are instances where there are only one dominating sound source. In the first step of the system  employ a feature detection and matching strategy. This produces TDOA data, possibly with missing data and with outliers. Then  use a robust and stratified approach for the parameter estimation.  use robust techniques to calculate initial estimates on the offsets parameters, follod by nonlinear optimization based on a rank criterion. Sequentially  use robust methods for calculating initial estimates of the sound source positions and microphone positions, follod by non-linear Maximum Likelihood estimation of all parameters. The methods are tested and verified using anechoic chamber sound recordings. (Less)"}]