2010Unpublished venueRequires access

Enhanced Binaural Loudspeaker Audio System with Room Modeling

Myung-Suk Song, Cha Zhang, Dinei A. F. Florencio, Hing-Goo Kang

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Abstract

For many years, spatial (3D) sound using headphones has been widely used in a number of applications. A rich spatial sensation is obtained by using head related transfer functions (HRTF) and playing the appropriate sound through headphones. In theory, loudspeaker audio systems would be capable of rendering 3D sound fields almost as rich as headphones, as long as the room impulse responses (RIRs) between the loudspeakers and the ears are known. In practice, however, obtaining these RIRs is hard, and the performance of loudspeaker based systems is far from perfect. New hope has been recently raised by a system that tracks the user’s head position and orientation, and incorporates them into the RIRs estimates in real time. That system made two simplifying assumptions: it used generic HRTFs, and it ignored room reverberation. In this paper we tackle the second problem: we incorporate a room reverberation estimate into the RIRs. Note that this is a nontrivial task: RIRs vary significantly with the listener’s positions, and even if one could measure them at a few points, they are notoriously hard to interpolate. Instead, we take an indirect approach: we model the room, and from that model we obtain an estimate of the main reflections. Position and characteristics of walls do not vary with the users’ movement, yet they allow to quickly compute an estimate of the RIR for each new user position. Of course the key question is whether the estimates are good enough. We show an improvement in localization perception of up to 32% (i.e., reducing average error from 23.5◦ to 15.9◦).

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What this paper is about

For many years, spatial (3D) sound using headphones has been widely used in a number of applications. A rich spatial sensation is obtained by using head related transfer functions (HRTF) and playing the appropriate sound through headphones. In theory, loudspeaker audio systems would be capable of rendering 3D sound fields almost as rich as headphones, as long as the room impulse responses (RIRs) between the loudspeakers and the ears are known. In practice, however, obtaining these RIRs is hard, and the performance of loudspeaker based systems is far from perfect. New hope has been recently raised by a system that tracks the user’s head position and orientation, and incorporates them into the RIRs estimates in real time. That system made two simplifying assumptions: it used generic HRTFs, and it ignored room reverberation. In this paper we tackle the second problem: we incorporate a room reverberation estimate into the RIRs. Note that this is a nontrivial task: RIRs vary significantly with the listener’s positions, and even if one could measure them at a few points, they are notoriously hard to interpolate. Instead, we take an indirect approach: we model the room, and from that model we obtain an estimate of the main reflections. Position and characteristics of walls do not vary with the users’ movement, yet they allow to quickly compute an estimate of the RIR for each new user position. Of course the key question is whether the estimates are good enough. We show an improvement in localization perception of up to 32% (i.e., reducing average error from 23.5◦ to 15.9◦).

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Available abstract

For many years, spatial (3D) sound using headphones has been widely used in a number of applications. A rich spatial sensation is obtained by using head related transfer functions (HRTF) and playing the appropriate sound through headphones. In theory, loudspeaker audio systems would be capable of rendering 3D sound fields almost as rich as headphones, as long as the room impulse responses (RIRs) between the loudspeakers and the ears are known. In practice, however, obtaining these RIRs is hard, and the performance of loudspeaker based systems is far from perfect. New hope has been recently raised by a system that tracks the user’s head position and orientation, and incorporates them into the RIRs estimates in real time. That system made two simplifying assumptions: it used generic HRTFs, and it ignored room reverberation. In this paper we tackle the second problem: we incorporate a room reverberation estimate into the RIRs. Note that this is a nontrivial task: RIRs vary significantly with the listener’s positions, and even if one could measure them at a few points, they are notoriously hard to interpolate. Instead, we take an indirect approach: we model the room, and from that model we obtain an estimate of the main reflections. Position and characteristics of walls do not vary with the users’ movement, yet they allow to quickly compute an estimate of the RIR for each new user position. Of course the key question is whether the estimates are good enough. We show an improvement in localization perception of up to 32% (i.e., reducing average error from 23.5◦ to 15.9◦).

Key concepts: Headphones, Loudspeaker, Binaural recording, Reverberation, Ambisonics, Computer science, Rendering (computer graphics), Head-related transfer function

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