A Binaural Sound Localization System Using Deep Convolutional Neural Networks
2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)(2019)
摘要
We propose a biologically inspired binaural sound localization system using a deep convolutional neural network (CNN) for reverberant environments. It utilizes a binaural Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) cochlear system to analyze binaural signals, a lateral inhibition function to sharpen temporal information of cochlear channels, and instantaneous correlation function on the two cochlear channels to encode binaural cues. The generated 2-D instantaneous correlation matrix (correlogram) encodes both interaural phase difference (IPD) cues and spectral information in a unified framework. Additionally, a sound onset detector is exploited to generate the correlograms only during sound onsets to remove interference from echoes. The onset correlograms are analyzed using a deep CNN for regression to the azimuthal angle of the sound. The proposed system was evaluated using experimental data in a reverberant environment, and displayed a root mean square localization error (RMSE) of 3.68 degrees in the -90 degrees to 90 degrees range.
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关键词
Electronic cochlea, MSO, IPD, Machine hearing, Deep learning, CNN, Neuromorphic engineering, Sound localization, Onset detection
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