Binaural localization of speech sources in 3-D using a composite feature vector of the HRTF

Binaural localization of speech sources in 3-D, using head-related transfer functions (HRTFs), always suffers elevation ambiguity due to the limited high frequency spectral information available at the receivers. This paper presents a method that overcomes this limitation by exploiting the interaural phase and magnitude features present in the HRTF. We (i) introduce a new feature vector that combines these two sets of features in a non-linear fashion, and (ii) propose a mechanism to extract this feature vector free from distortion by the speech spectra. The performance of the proposed method is evaluated and compared with a correlation-based HRTF database matching approach and a two-step localization technique for multiple source positions, HRTFs (individuals) and speech inputs. The results suggest that up to 20% improvement in localization performance can be achieved for moderate signal-to-noise ratios.