HO-SIRR
Application description
Spatial Impulse Response Rendering (SIRR) is a method for converting Ambisonic room impulse responses (RIRs) into loudspeaker array RIRs [1,2]. The method makes assumptions regarding the composition of the spatial RIR and extracts parameters over time, which are used to map the input to the output in an adaptive and more informed manner. This is essentially the RIR equivalent of Directional Audio Coding (DirAC; which instead operates on real-time Ambisonic signals).
The idea is that you then convolve a monophonic source with this loudspeaker array RIR, and it will be reproduced and exhibit the spatial characteristics of the captured space. Note that the HO-SIRR algorithm is an extention of the original first-order SIRR formulation, first proposed back in 2005 [3,4], by employing the higher-order analysis principles described in [5], which permits higher spatial accuracy during the mapping provided that higher-order components are available.
This HO-SIRR application is a direct port of the HO-SIRR MATLAB toolbox, which can be found here.
The suggested workflow is:
- Measure a room impulse response (RIR) of a space with a spherical microphone array (e.g. using HAART), and convert it into an Ambisonic/B-format RIR (e.g. using sparta_array2sh).
- Load this B-Format/Ambisonic RIR into the HOSIRR App/plug-in and specify your loudspeaker array directions and desired rendering configuration (although, the default should suffice for most purposes).
- Click “Render”, and then “Save”, to export the resulting loudspeaker array RIR as a multi-channel .wav file.
- Then simply convolve this loudspeaker array RIR with a monophonic source signal, and it will be reproduced over the loudspeaker array (also exhibiting the spatial characteristics of the captured space). Plug-ins such as Xvolver, X-MCFX, sparta_matrixconv (included in the installer), and mcfx_convolver, are well suited to this convolution task.
Listening test results at a glance
The perceptual performance of HO-SIRR was evaluated based on formal listening tests in [1], where it was compared to Mode-Matching Ambisonics decoding. It was found that if the mono signal is quite stationary (such as a trombone recording), then first-order SIRR renderings can sound almost equivalent to 5th order Ambisonics. However, if the mono signal is more transient (such as a kick drum or speech sample), then the benefits of the higher-order SIRR renderings are revealed. For an in-depth description of the listening test and a discussion of the results, see: [1].
About the authors
- Leo McCormack: former postdoctoral researcher at Aalto University.
- Archontis Politis: professor at Tampere University.
- Ville Pulkki: professor at Aalto University.
License
This application may be used for academic, personal, and/or commercial use. The source code may also be used for commercial purposes, provided that the terms of the GPLv3 license are honoured. This requires that the original code and/or any derived works must also be open-sourced and made available under the same GPLv3 license, if it is to be used for commercial purposes.
References
[1] McCormack, L., Pulkki, V., Politis, A., Scheuregger, O. and Marschall, M. (2020). Higher-Order Spatial Impulse Response Rendering: Investigating the Perceived Effects of Spherical Order, Dedicated Diffuse Rendering, and Frequency Resolution.
Journal of the Audio Engineering Society, 68(5), pp.338-354.
[2] McCormack, L., Politis, A., Scheuregger, O., and Pulkki, V. (2019). Higher-order processing of spatial impulse responses.
Proceedings of the 23rd International Congress on Acoustics, 9–13 September 2019 in Aachen, Germany.
[3] Merimaa, J. and Pulkki, V. (2005). Spatial impulse response rendering I: Analysis and synthesis
Journal of the Audio Engineering Society, 53(12), pp.1115-1127.
[4] Pulkki, V. and Merimaa, J. (2006). Spatial impulse response rendering II: Reproduction of diffuse sound and listening tests
Journal of the Audio Engineering Society, 54(1/2), pp.3-20.
[5] Politis, A. and Pulkki, V. (2016). Acoustic intensity, energy-density and diffuseness estimation in a directionally-constrained region
arXiv preprint arXiv:1609.03409.