Extraction of ambience sound from microphone array recordings for spatialisation
Extraction of ambience sound from microphone array recordings for spatialisation
It is often desirable to separate main (primary) sound sources and background sound in microphone array recordings where the microphone signals are a combination of these two components, and then render primary and background sounds using different rendering techniques. Considerable research has investigated the extraction of the primary sources from the mixture using source extraction and localisation techniques, to then re-spatialise the extracted signals using the desired spatial audio reproduction format (for example, loudspeaker panning, Ambisonics or binauralisation). Little research, however, has focused on the separation and spatialisation of the ambience (background) component of the mixture, like secondary sources, diffuse field, or reverberation. This component is crucial for an accurate and realistic reconstruction of the overall 3D audio scene. This paper presents a new approach to extracting a spatialised version of the background field from a set of microphone array signals containing the original sound field mixture. The approach utilises an LMS algorithm to perform the separation between the primary sources and background sound. The fundamental theory of this technique is presented and validated with experimental measurements.
Ambience Extraction, LMS, Microphone Arrays
Paul, Vlad-Stefan
a643f880-7e70-4ae0-a27b-4e77c3c451de
Hahn, Nara
9c5cb8ff-b351-40ff-974b-9635a790ec16
Hollebon, Jacob
75e4dd71-cfb5-4d28-82a5-7ee1bee73207
2023
Paul, Vlad-Stefan
a643f880-7e70-4ae0-a27b-4e77c3c451de
Hahn, Nara
9c5cb8ff-b351-40ff-974b-9635a790ec16
Hollebon, Jacob
75e4dd71-cfb5-4d28-82a5-7ee1bee73207
Paul, Vlad-Stefan, Hahn, Nara and Hollebon, Jacob
(2023)
Extraction of ambience sound from microphone array recordings for spatialisation.
In 2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA): from Architecture to Automotive, I3DA 2023.
IEEE..
(doi:10.1109/I3DA57090.2023.10289397).
Record type:
Conference or Workshop Item
(Paper)
Abstract
It is often desirable to separate main (primary) sound sources and background sound in microphone array recordings where the microphone signals are a combination of these two components, and then render primary and background sounds using different rendering techniques. Considerable research has investigated the extraction of the primary sources from the mixture using source extraction and localisation techniques, to then re-spatialise the extracted signals using the desired spatial audio reproduction format (for example, loudspeaker panning, Ambisonics or binauralisation). Little research, however, has focused on the separation and spatialisation of the ambience (background) component of the mixture, like secondary sources, diffuse field, or reverberation. This component is crucial for an accurate and realistic reconstruction of the overall 3D audio scene. This paper presents a new approach to extracting a spatialised version of the background field from a set of microphone array signals containing the original sound field mixture. The approach utilises an LMS algorithm to perform the separation between the primary sources and background sound. The fundamental theory of this technique is presented and validated with experimental measurements.
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More information
e-pub ahead of print date: 23 October 2023
Published date: 2023
Additional Information:
Funding Information:
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC,UKRI) EP/R513325/1.
Publisher Copyright:
© 2023 IEEE.
Venue - Dates:
2023 Immersive and 3D Audio: from Architecture to Automotive: I3DA 2023, University of Bologna, Bologna, Italy, 2023-09-05 - 2023-09-07
Keywords:
Ambience Extraction, LMS, Microphone Arrays
Identifiers
Local EPrints ID: 484449
URI: http://eprints.soton.ac.uk/id/eprint/484449
PURE UUID: 904660bb-5f07-40b1-bb72-56eddf97943d
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Date deposited: 16 Nov 2023 12:13
Last modified: 18 Mar 2024 04:05
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Author:
Nara Hahn
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