Abstract
The increasing importance of spatial audio technologies has demonstrated the need and importance of correctly adapting to the individual characteristics of the human auditory system, and illustrates the crucial need for humanoid localization systems for testing these technologies. To this end, this paper introduces a novel feature analysis and selection approach for binaural localization and builds a probabilistic localization mapping model, especially useful for the vertical dimension localization. The approach uses the mutual information as a metric to evaluate the most significant frequencies of the interaural phase difference and interaural level difference. Then, by using the random forest algorithm and embedding the mutual information as a feature selection criteria, the feature selection procedures are encoded with the training of the localization mapping. The trained mapping model is capable of using interaural features more efficiently, and, because of the multiple-tree-based model structure, the localization model shows robust performance to noise and interference. By integrating the direct path relative transfer function estimation, we propose to devise a novel localization approach that has improved performance in the presence of noise and reverberation. The proposed mapping model is compared with the state-of-the-art manifold learning procedure in different acoustical configurations, and a more accurate and robust output can be observed.
Highlights
Spatial audio technologies has shown its importance in various fields, such as video conferencing, virtual reality, humanoid robot interactions and hearing aids, and there are many existing methods reproducing a spatial sound field on different devices for human listeners
By integrating the direct path relative transfer function estimation, we propose to devise a novel localization approach that has improved performance in the presence of noise and reverberation
Much behavioural and psychoacoustic evidence has confirmed that two individualized interaural cues, Interaural Time Difference (ITD) and Interaural Level Difference (ILD), play an essential role in localizing a sound source
Summary
Spatial audio technologies has shown its importance in various fields, such as video conferencing, virtual reality, humanoid robot interactions and hearing aids, and there are many existing methods reproducing a spatial sound field on different devices for human listeners. Weng et al adopted a non-parametric tree-based learning method to retrieve the mapping between the interaural cues and source locations with fewer restrictions on its spatiotemporal characteristics and environment structure [19]. Deleforge et al proved the elevation ambiguities of Interaural Phase Difference (IPD) at the high-frequency range (between 2 to 8 kHz) and used a feature space concatenated by full-spectrum ILD and low-frequency IPD [17]. Another challenge in sound source localization is localizing the source in the presence of noise and reverberation. Model proposed in [17] in different noise and reverberation environments without prior knowledge of the acoustical conditions
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