Abstract
The paper considers the task of recognizing the category of a context surrounding an audio sensor. Due to the unstructured and diverse nature of the auditory context and constituent environmental sounds, which differs from the usual structured audio data like speech or music, the recognition of auditory context faces many difficulties and relatively fewer researchs have addressed it. In this paper, we propose an ensemble recognition scheme based on the Hough forest framework for unstructured auditory contexts, which combines the discriminative and generative modeling of the context. We learn the effective audio feature representation for environmental sounds in the context with the LDB algorithm, and recognize the context using the Hough forest based ensemble classifier, which aggregates both the segmental and the contextual probabilistic votes on the context category by the segments of the auditory context. The experimental results demonstrate the effectiveness of the proposed approach for auditory context recognition.
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Su, F., Yang, L. (2013). Auditory Context Recognition Combining Discriminative and Generative Models. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_56
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DOI: https://doi.org/10.1007/978-3-319-03731-8_56
Publisher Name: Springer, Cham
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