Semantic feature extraction based on subspace learning with temporal constraints for acoustic event recognition

In acoustic event recognition (AER), it is important to extract semantic features. As two crucial aspects of semantic features, the essential content and the temporal structure can strongly affect the understanding of humans and even computers. In this paper, we first divide each acoustic event sample into short segments. Then, for jointly considering the above two aspects, two semantic feature extraction methods are proposed by learning a low-dimensional subspace. The first method, named subspace learning with temporal constraints (SLOC), is designed for not only preserving the essential content by a low-rank approximation scheme but also capturing the temporal structure between every two chronologically ordered segments. This temporal structure is encoded by forcing the corresponding projection coefficients associated with different elements of the subspace basis to increase separately. The second method, named non-negative sparse SLOC (NSSLOC), is proposed by introducing two constraints into the basis of SLOC. Specifically, a non-negative constraint is designed to better guarantee the low-rank approximation, and a row-wise sparse constraint is employed to implement a reasonable feature selection when calculating the projection coefficients. Moreover, we propose two optimization algorithms for our methods. For each acoustic event sample, the subspace basis learned by either of our methods is adopted as semantic features that are further used for classification. Finally, the proposed methods are evaluated on the AudioEvent and the ESC-50 databases. The experimental results indicate that our methods are better than or competitive with the related state-of-the-art methods.