Conference Paper

Amplitude Panning and the Interior Pan

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Abstract

The perception of source location using multi-loudspeaker amplitude panning is considered. While there exist many perceptual models for pairwise panning, relatively few studies consider the general multi-loudspeaker case. This paper evaluates panning scenarios in which a source is panned on the boundary or within the volume bounded by discrete loudspeakers, referred to as boundary and interior pans respectively. Listening results reveal the following: 1) pans to a single loudspeaker yield lowest localization error, 2) pairwise pans tend to be consistently localized closer to the listener than single loudspeaker pans, 3) largest errors occur when the virtual source is panned close to the listener, 4) interior pans are accurately perceived and, surprisingly, in some cases more accurately than pairwise pans.

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... Thomas and Robinson [35] conducted a study using physical loudspeakers as anchors in a localization task assessing dual-balance panned, auditory events. Five loudspeakers were configured at positions +90 • , +45 • , 0 • , -45 • and -90 • . ...
... The maximum eight loudspeakers are comprised of four pairs, two for left/right and two for front/rear. The gains for each balance pair are computed using sine/cosine functions [35]. The Cartesian point source panner is a 3D extension of the dual-balance panner, as described in [16]. ...
Conference Paper
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Interior panning algorithms enable content authors to position auditory events not only at the periphery of the loudspeaker configuration, but also within the internal space between the listeners and the loudspeakers. In this study such algorithms are rigorously evaluated, comparing rendered static auditory events at various locations against true physical loudspeaker references. Various algorithmic approaches are subjectively assessed in terms of; Overall, Timbral, and Spatial Quality for three different stimuli, at five different positions and three radii. Results show for static positions that standard Vector Base Amplitude Panning performs equal, or better, than all other interior panning algorithms tested here. Timbral Quality is maintained throughout all distances. Ratings for Spatial Quality vary, with some algorithms performing significantly worse at closer distances. Ratings for Overall Quality reduce moderately with respect to reduced reproduction radius and are predominantly influenced by Timbral Quality.
... As we further discuss in Sect. 2, the renderer (a fully-differentiable implementation [12] of Dolby Atmos [13]) enables multichannel-based learning, as opposed to object-based learning, because it dictates a fully-determined object-based format at the encoder's output. Finally, note that multichannel-based learning also bears resemblances with self-supervised learning [14,15]: instead of directly learning from objects, our loss targets a proxy signal based on multichannel renders. ...
... The mask-estimation block implements the U-Net [16], with minimal adaptations in the final layer to generate 5.1 masks. The renderer is the Dolby Atmos renderer [13] expressed in differentiable form. The de-panner and de-trimmer also correspond to the Dolby Atmos renderer. ...
Preprint
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The current paradigm for creating and deploying immersive audio content is based on audio objects, which are composed of an audio track and position metadata. While rendering an object-based production into a multichannel mix is straightforward, the reverse process involves sound source separation and estimating the spatial trajectories of the extracted sources. Besides, cinematic object-based productions are often composed by dozens of simultaneous audio objects, which poses a scalability challenge for audio object extraction. Here, we propose a novel deep learning approach to object extraction that learns from the multichannel renders of object-based productions, instead of directly learning from the audio objects themselves. This approach allows tackling the object scalability challenge and also offers the possibility to formulate the problem in a supervised or an unsupervised fashion. Since, to our knowledge, no other works have previously addressed this topic, we first define the task and propose an evaluation methodology, and then discuss under what circumstances our methods outperform the proposed baselines.
... In stereo or multichannel loudspeaker setups, a virtual or phantom source is a sound that appears to emanate from a position other than the physical loudspeaker locations [1]. The most common rendering techniques for creating such phantom sources are based on stereo amplitude panning and their multichannel extensions (e.g., vector-base amplitude panning [2], dual/triple balance amplitude panning [3], distance-based amplitude panning [4]). These panning methods distribute the source signal among several loudspeakers, assigning a gain to each loudspeaker so that the resulting sound mixture creates the illusion of a phantom sound source coming from the intended direction. ...
Conference Paper
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... In stereo or multichannel loudspeaker setups, a virtual or phantom source is a sound that appears to emanate from a position other than the physical loudspeaker locations [1]. The most common rendering techniques for creating such phantom sources are based on stereo amplitude panning and their multichannel extensions (e.g., vector-base amplitude panning [2], dual/triple balance amplitude panning [3], distance-based amplitude panning [4]). These panning methods distribute the source signal among several loudspeakers, assigning a gain to each loudspeaker so that the resulting sound mixture creates the illusion of a phantom sound source coming from the intended direction. ...
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