Low latency localization of multiple sound sources in reverberant environments

Abstract

Sound source localization algorithms determine the physical position of a sound source in respect to a listener. For practical applications, a localization algorithm design has to take into account real world conditions like multiple active sources, reverberation, and noise. The application can impose additional constraints on the algorithm, e.g., a requirement for low latency. This work defines the most important constraints for practical applications, introduces an algorithm, which tries to fulfill all requirements as good as possible, and compares it to state-of-the-art sound source localization approaches.

Development of an integrated multi-modal communication robotic face

“…The noisy localization estimates are postprocessed with Sequential Monte Carlo (SMC) simulations to remove outliers and robustly track the source positions over time. For more in-depth explanation please see [12].…”

A spherical cross-channel algorithm for binaural sound localization

“…This variance is then modified in order to obtain the desired SNR dB value, see Eq. (16). Because SNR dB is defined in the free-field case, the effective SNR measured on the left and right signals, denoted SNR L and SNR R respectively, are dependent of the source azimuth θ s , as illustrated in Figure 2.…”

“…As before, a realistic isotropic noise is added to the simulated sensed signals, along the lines of Eq. (16). The simulation of the noise is still performed with 200 noise sources placed all around the head, according to Eq.…”

“…The approach proposed in this paper is a generalization of an idea first depicted in [15] and extended in [16], [17]. Instead of working with the estimated binaural cues, the algorithm proposed here directly applies to the two binaural signals, using HRTFs derived from a generic spherical head model [18].…”

Comparison of head-related impulse response measurement approaches

“…In the last decades, HRTF-based techniques have become prominent in acoustic signal processing for various telepresence applications, e.g., binaural sound localization and synthesis 1 and binaural robotic sound source localization. 2 As each individual has, in general, a unique body geometry, the corresponding HRTFs are naturally different from person to person.…”