By the same authors

From the same journal

From the same journal

EigenScape: A Database of Spatial Acoustic Scene Recordings

Research output: Contribution to journalSpecial issuepeer-review

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EigenScape : A Database of Spatial Acoustic Scene Recordings. / Green, Marc Ciufo; Murphy, Damian Thomas.

In: Applied Sciences, Vol. 7, No. 11, 1204, 22.11.2017.

Research output: Contribution to journalSpecial issuepeer-review

Harvard

Green, MC & Murphy, DT 2017, 'EigenScape: A Database of Spatial Acoustic Scene Recordings', Applied Sciences, vol. 7, no. 11, 1204. https://doi.org/10.3390/app7111204

APA

Green, M. C., & Murphy, D. T. (2017). EigenScape: A Database of Spatial Acoustic Scene Recordings. Applied Sciences, 7(11), [1204]. https://doi.org/10.3390/app7111204

Vancouver

Green MC, Murphy DT. EigenScape: A Database of Spatial Acoustic Scene Recordings. Applied Sciences. 2017 Nov 22;7(11). 1204. https://doi.org/10.3390/app7111204

Author

Green, Marc Ciufo ; Murphy, Damian Thomas. / EigenScape : A Database of Spatial Acoustic Scene Recordings. In: Applied Sciences. 2017 ; Vol. 7, No. 11.

Bibtex - Download

@article{10d11097b57f4d54b1ceb4ae9a0822c5,
title = "EigenScape: A Database of Spatial Acoustic Scene Recordings",
abstract = "The classification of acoustic scenes and events is an emerging area of research in the field of machine listening. Most of the research conducted so far uses spectral features extracted from monaural or stereophonic audio rather than spatial features extracted from multichannel recordings. This is partly due to the lack thus far of a substantial body of spatial recordings of acoustic scenes. This paper formally introduces EigenScape, a new database of fourth-order Ambisonic recordings of eight different acoustic scene classes. The potential applications of a spatial machine listening system are discussed before detailed information on the recording process and dataset are provided. A baseline spatial classification system using directional audio coding (DirAC) techniques is detailed and results from this classifier are presented. The classifier is shown to give good overall scene classification accuracy across the dataset, with 7 of 8 scenes being classified with an accuracy of greater than 60% with an 11% improvement in overall accuracy compared to use of Mel-frequency cepstral coefficient (MFCC) features. Further analysis of the results shows potential improvements to the classifier. It is concluded that the results validate the new database and show that spatial features can characterise acoustic scenes and as such are worthy of further investigation",
author = "Green, {Marc Ciufo} and Murphy, {Damian Thomas}",
note = "{\textcopyright} 2017 by the authors.",
year = "2017",
month = nov,
day = "22",
doi = "10.3390/app7111204",
language = "English",
volume = "7",
journal = "Applied Sciences",
issn = "2076-3417",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "11",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - EigenScape

T2 - A Database of Spatial Acoustic Scene Recordings

AU - Green, Marc Ciufo

AU - Murphy, Damian Thomas

N1 - © 2017 by the authors.

PY - 2017/11/22

Y1 - 2017/11/22

N2 - The classification of acoustic scenes and events is an emerging area of research in the field of machine listening. Most of the research conducted so far uses spectral features extracted from monaural or stereophonic audio rather than spatial features extracted from multichannel recordings. This is partly due to the lack thus far of a substantial body of spatial recordings of acoustic scenes. This paper formally introduces EigenScape, a new database of fourth-order Ambisonic recordings of eight different acoustic scene classes. The potential applications of a spatial machine listening system are discussed before detailed information on the recording process and dataset are provided. A baseline spatial classification system using directional audio coding (DirAC) techniques is detailed and results from this classifier are presented. The classifier is shown to give good overall scene classification accuracy across the dataset, with 7 of 8 scenes being classified with an accuracy of greater than 60% with an 11% improvement in overall accuracy compared to use of Mel-frequency cepstral coefficient (MFCC) features. Further analysis of the results shows potential improvements to the classifier. It is concluded that the results validate the new database and show that spatial features can characterise acoustic scenes and as such are worthy of further investigation

AB - The classification of acoustic scenes and events is an emerging area of research in the field of machine listening. Most of the research conducted so far uses spectral features extracted from monaural or stereophonic audio rather than spatial features extracted from multichannel recordings. This is partly due to the lack thus far of a substantial body of spatial recordings of acoustic scenes. This paper formally introduces EigenScape, a new database of fourth-order Ambisonic recordings of eight different acoustic scene classes. The potential applications of a spatial machine listening system are discussed before detailed information on the recording process and dataset are provided. A baseline spatial classification system using directional audio coding (DirAC) techniques is detailed and results from this classifier are presented. The classifier is shown to give good overall scene classification accuracy across the dataset, with 7 of 8 scenes being classified with an accuracy of greater than 60% with an 11% improvement in overall accuracy compared to use of Mel-frequency cepstral coefficient (MFCC) features. Further analysis of the results shows potential improvements to the classifier. It is concluded that the results validate the new database and show that spatial features can characterise acoustic scenes and as such are worthy of further investigation

U2 - 10.3390/app7111204

DO - 10.3390/app7111204

M3 - Special issue

VL - 7

JO - Applied Sciences

JF - Applied Sciences

SN - 2076-3417

IS - 11

M1 - 1204

ER -