Automatic Spatial Audio Scene Classification in Binaural Recordings of Music

Sławomir Zieliński, Hyunkook Lee

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Article number1724
Number of pages22
JournalApplied Sciences (Switzerland)
Issue number9
Early online date26 Apr 2019
Publication statusPublished - 1 May 2019


Dive into the research topics of 'Automatic Spatial Audio Scene Classification in Binaural Recordings of Music'. Together they form a unique fingerprint.

Cite this