Sound Externalization: A Review of Recent Research

Abstract

Sound externalization, or the perception that a sound source is outside of the head, is an intriguing phenomenon that has long interested psychoacousticians. While previous reviews are available, the past few decades have produced a substantial amount of new data.In this review, we aim to synthesize those data and to summarize advances in our understanding of the phenomenon. We also discuss issues related to the definition and measurement of sound externalization and describe quantitative approaches that have been taken to predict the outcomes of externalization experiments. Last, sound externalization is of practical importance for many kinds of hearing technologies. Here, we touch on two examples, discussing the role of sound externalization in augmented/virtual reality systems and bringing attention to the somewhat overlooked issue of sound externalization in wearers of hearing aids.

Keywords: distal attribution; in-head localization; sound image externalization; spatial perception; virtual acoustics.

Figure 1.

Sound externalization (A) as a binary decision, (B) as a continuum, and (C) depending on lateral position.

Figure 2.

Mean externalization ratings from three studies showing the tendency for increased externalization with sound source laterality. Brimijoin et al. (2013, N = 11): Ratings correspond to the proportion of externalized responses obtained using a binary judgment and are averaged across fullband and lowpass conditions. Small head movements were allowed, and the sources were either fixed in azimuth (“world-fixed”) or moved along with the head movements (“head-fixed”). Kates et al. (2018, N = 20): Ratings were obtained using a 100-point scale with visual references and are shown separately for anechoic and reverberant conditions. Leclère et al. (2019, N = 21): Ratings were obtained using a continuous percentage scale with eyes closed and are shown separately for anechoic and reverberant conditions (the latter averaged across four rooms). Error bars show standard errors of the mean.

Figure 3.

Effects of spectral smoothing methods on an example HRTF at 60° azimuth. Top row: monaural spectrum at the ipsilateral ear. Bottom row: difference between the left- and right-ear spectra (or the ILD). Spectral representations were obtained by filtering the impulse response with a gammatone filter bank (Lyon, 1997) and calculating the root mean square amplitude within each band. Left column: smoothing according to Hassager et al. (2016) where B is the spectral smoothing bandwidth relative to one ERB_N (Glasberg & Moore, 1990). Right column: smoothing according to Baumgartner et al. (2017) where C is a scaling factor that is applied to spectral magnitudes. Gray-shaded areas indicate frequency ranges not tested in the respective experiments. The HRTF is from subject S01 in Baumgartner et al. (2017). ILD = interaural level difference.

Figure 4.

General structure of template-based externalization models that calculate the deviation of acoustical cues from those of a natural, well-externalized, reference stimulus. Models focused on the direct sound use long-term excitation patterns and involve comparisons of monaural cues at each ear and/or interaural cues (e.g., Baumgartner & Majdak, 2020; Hassager et al. 2016). Models focused on reverberation use short-term excitation patterns and involve only interaural-cue comparisons (e.g., Li et al. 2018).