Acoustic Scene Classification Using Spatial Features

Research output: Contribution to conferencePaper




Conference2017 Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE2017),
Conference date(s)16/11/1717/01/18
Internet address

Publication details

DatePublished - 16 Nov 2017
Number of pages4
Original languageEnglish


Due to various factors, the vast majority of the research in the
field of Acoustic Scene Classification has used monaural or binaural
datasets. This paper introduces EigenScape - a new dataset
of 4th-order Ambisonic acoustic scene recordings - and presents
preliminary analysis of this dataset. The data is classified using a
standard Mel-Frequency Cepstral Coefficient - Gaussian Mixture
Model system, and the performance of this system is compared to
that of a new system using spatial features extracted using Directional
Audio Coding (DirAC) techniques. The DirAC features are
shown to perform well in scene classification, with some subsets
of these features outperforming the MFCC classification. The differences
in label confusion between the two systems are especially
interesting, as these suggest that certain scenes that are spectrally
similar might not necessarily be spatially similar.

    Research areas

  • Acoustic scene classification, ambisonics, soundscape

Research outputs

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations