any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTWe present a novel approach to scene classification using combined audio signal and video image features and compare this methodology to scene classification results using each modality in isolation. Each modality is represented using summary features, namely Mel-frequency Cepstral Coefficients (audio) and Scale Invariant Feature Transform (SIFT) (video) within a multi-resolution bag-offeatures model. Uniquely, we extend the classical bag-ofwords approach over both audio and video feature spaces, whereby we introduce the concept of compressive sensing as a novel methodology for multi-modal fusion via audiovisual feature dimensionality reduction. We perform evaluation over a range of environments showing performance that is both comparable to the state of the art (86%, over ten scene classes) and invariant to a ten-fold dimensionality reduction within the audio-visual feature space using our compressive representation approach.