Real time humanoid sound source localization and tracking in a highly reverberant environment

Abstract

An algorithm for real time humanoid sound localization and tracking using only two microphones in a highly reverberant environment is proposed. Several recently developed 3D humanoid sound localization algorithms require the environment to be anechoic. Also, the resolution of front-back ambiguity problem during sound localization requires the knowledge about the reference signals. Using HRTF based sound localization together with extended kalman filtering, we are able to accurately track moving sound sources in real time in a highly reverberant environment. This algorithm uses only two microphones and requires no prior knowledge of the reference signals.

Real-time implementation and performance optimization of 3D sound localization on GPUs

“…The implementation of sound localization is based on the TDOA approach. Real-time 3D sound localization has been implemented in [10] based on extended Kaman filter for two microphones. However, the localization performance tends to degrade significantly when the reverberation or noise levels increase.…”

Towards Human-Like Production and Binaural Localization of Speech Sounds in Humanoid Robots

“…There has been considerable interest recently in sound source localization procedures [1] [2] for robotic agents [3] [4] [5] [6] or telepresence applications [7]; however, the related literature on binaural sound perception and localization in humanoid robotics is rather sparse [8] [9]. Moreover, attempts to establish artificial binaural sound perception systems motivated by human audition are, arguably, still in their initial phases [5] [10].…”

HRTF Sound Localization

“…The improvement is that the ratio of HRTFs does not need to be inverted and can be precomputed and stored in memory Usman et al, 2008). …”

“…To avoid the instability problem, this approach is to exploit the associative property of convolution operator (Usman et al, 2008). Figure 6 illustrates the single-source cross-convolution localization approach.…”

“…A set of time and measurement update equations are used to predict the next state (Kalman, 1960). The state vector is defined by current location coordinates x and y and the velocity components v x and v y (Potamitis et al, 2004;Usman et al, 2008). Note that here the predictor is applied to two dimensional space.…”