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The conventional room geometry blind inference techniques with acousticsignals are conducted based on the prior knowledge of the environment, such asthe room impulse response (RIR) or the sound source position, which will limitits application under unknown scenarios. To solve this problem, we haveproposed a room geometry reconstruction method in this paper by using thegeometric relation between the direct signal and first-order reflections. Inaddition to the information of the compact microphone array itself, this methoddoes not need any precognition of the environmental parameters. Besides, thelearning-based DNN models are designed and used to improve the accuracy andintegrity of the localization results of the direct source and first-orderreflections. The direction of arrival (DOA) and time difference of arrival(TDOA) information of the direct and reflected signals are firstly estimatedusing the proposed DCNN and TD-CNN models, which have higher sensitivity andaccuracy than the conventional methods. Then the position of the sound sourceis inferred by integrating the DOA, TDOA and array height using the proposedDNN model. After that, the positions of image sources and correspondingboundaries are derived based on the geometric relation. Experimental results ofboth simulations and real measurements verify the effectiveness and accuracy ofthe proposed techniques compared with the conventional methods under differentreverberant environments.

Room geometry blind inference based on the localization of real sound source and first order reflections