In this paper, we propose a novel approach for segmenting and tokenizing a video scene recording into a sequence of cascade units, known as visual segment units and modeled with visual segment models (VSMs) for video scene classification (VSC). Specifically, the proposed VSM framework takes deep visual features extracted from pre-trained encoders as inputs and models the temporal interactions between segment units by hidden Markov models. Next, we use unit co-occurrence statistics to introduce relationships between VSM units within a video scene recording. Furthermore, the VSM approach is extended to an acoustic-visual variant, subsequently integrating itself into a deep learning-based multi-modal scene classification system. This combination serves to further exploit the complementary nature of audio and video data. By incorporating a set of visual segment units into modeling a video scene class, it captures both inter-class similarity and intra-class diversity, facilitating improved scene classification, especially within categories prone to confusion. Extensive experimental results on a benchmark published by the DCASE (Detection and Classification of Acoustic Scenes and Events) 2021 Challenge show that the proposed framework can effectively handle the confusion issue among similar video scenes. In addition, our multi-modal integration system achieves state-of-the-art performance in the audio-visual scene classification task in the DCASE 2021 Challenge, thereby demonstrating the effectiveness of our proposed approach.

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system leverages on an ad-hoc score combination based on two CNN classifiers: (i) the first CNN classifies acoustic inputs into one of three broad classes, and (ii) the second CNN classifies the same inputs into one of ten finergrained classes. Three different CNN architectures are explored to implement the two-stage classifiers, and a frequency sub-sampling scheme is investigated. Moreover, novel data augmentation schemes for ASC are also investigated. Evaluated on DCASE 2020 Task 1a, our results show that the proposed ASC system attains a state-of-the-art accuracy on the development set, where our best system, a two-stage fusion of CNN ensembles, delivers a 81.9% average accuracy among multi-device test data, and it obtains a significant improvement on unseen devices. Finally, neural saliency analysis with class activation mapping (CAM) gives new insights on the patterns learnt by our models.