The classification of acoustic scenes is important in emerging applications such as automatic audio surveillance, machine listening and multimedia content analysis. In this paper, we present an approach for acoustic scene classification by using joint time-frequency image-based feature representations. In acoustic scene classification, joint time-frequency representation (TFR) is shown to better represent important information across a wide range of low and middle frequencies in the audio signal. The audio signal is converted to Constant-Q Transform (CQT) and Mel-spectrum TFRs and local binary patterns (LBP) are used to extract the features from these TFRs. To ensure localized spectral information is not lost, the TFRs are divided into a number of zones. Then, we perform score level fusion to further improve the classification performance accuracy. Our technique achieves a competitive performance with a classification accuracy of 83.4% on the DCASE 2016 development dataset compared to the existing current state of the art.

This paper presents an approach for acoustic scene classification using the local binary pattern (LBP) and random forest (RF). The audio signal is converted to a Constant-Q transform (CQT) representation and LBP is used to extract the features from this time-frequency representation. The CQT representations are divided into a number of sub-bands to obtain more localized features relevant to the spectral information. We then use random forest to select the most important features for each band of extracted LBP features. For further performance enhancement, we use feature level fusion of LBP and HOG features. The proposed system has achieved an accuracy of 85% on the DCASE 2016 dataset.

In this paper, we present an approach for acoustic scene classification, which aggregates spectral and temporal features. We do this by proposing the first use of the variable-Q transform (VQT) to generate the time-frequency representation for acoustic scene classification. The VQT provides finer control over the resolution compared to the constant-Q transform (CQT) or short time fourier transform and can be tuned to better capture acoustic scene information. We then adopt a variant of the local binary pattern (LBP), the adjacent evaluation completed LBP (AECLBP), which is better suited to extracting features from acoustic time-frequency images. Our results yield a 5.2% improvement on the DCASE 2016 dataset compared to the application of standard CQT with LBP. Fusing our proposed AECLBP with HOG features, we achieve a classification accuracy of 85.5%, which outperforms one of the top performing systems.

This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relevant local features which better characterize the audio TFRs. To further improve the classification performance, we perform feature and score level fusion of the proposed LBP (with zoning) with histogram of gradients (HOG) of the TFR images. Our technique demonstrates an improved performance by achieving a classification accuracy of 95.2% using a fusion of time-frequency derived features.