Automatic Spatial Audio Scene Classification in Binaural Recordings of Music

Sławomir Zieliński , Hyunkook Lee

Abstract

The aim of the study was to develop a method for automatic classification of the three spatial audio scenes, differing in horizontal distribution of foreground and background audio content around a listener in binaurally rendered recordings of music. For the purpose of the study, audio recordings were synthesized using thirteen sets of binaural-room-impulse-responses (BRIRs), representing room acoustics of both semi-anechoic and reverberant venues. Head movements were not considered in the study. The proposed method was assumption-free with regards to the number and characteristics of the audio sources. A least absolute shrinkage and selection operator was employed as a classifier. According to the results, it is possible to automatically identify the spatial scenes using a combination of binaural and spectro-temporal features. The method exhibits a satisfactory classification accuracy when it is trained and then tested on different stimuli but synthesized using the same BRIRs (accuracy ranging from 74% to 98%), even in highly reverberant conditions. However, the generalizability of the method needs to be further improved. This study demonstrates that in addition to the binaural cues, the Mel-frequency cepstral coefficients constitute an important carrier of spatial information, imperative for the classification of spatial audio scenes.
Author Sławomir Zieliński (FCS / DDMCG)
Sławomir Zieliński,,
- Department of Digital Media and Computer Graphics
, Hyunkook Lee
Hyunkook Lee,,
-
Journal seriesApplied Sciences-Basel, ISSN 2076-3417, (N/A 70 pkt)
Issue year2019
Vol9
No9
Pages1-22
Publication size in sheets1.05
Keywords in Englishbinaural audio, machine-listening, machine-learning, spatial audio scene classification
ASJC Classification1507 Fluid Flow and Transfer Processes; 1508 Process Chemistry and Technology; 1706 Computer Science Applications; 2200 General Engineering; 2500 General Materials Science; 3105 Instrumentation
DOIDOI:10.3390/app9091724
Internal identifier000045341
Languageen angielski
LicenseJournal (articles only); published final; Uznanie Autorstwa (CC-BY); with publication
Score (nominal)70
Score sourcejournalList
ScoreBUT score = 30.0, 04-06-2019, manual
Ministerial score = 70.0, 04-03-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.985; WoS Impact Factor: 2018 = 2.217 (2) - 2018=2.287 (5)
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