Abstract: The present disclosure provides a weakly-supervised sound event detection method and system based on adaptive hierarchical pooling. The system includes an acoustic model and an adaptive hierarchical pooling algorithm module (AHPA-model), where the acoustic model inputs a pre-processed and feature-extracted audio signal, and predicts a frame-level prediction probability aggregated by the AHPA-module to obtain a sentence-level prediction probability. The acoustic model and a relaxation parameter are jointly optimized to obtain an optimal model weight and an optimal relaxation parameter based for formulating each category of sound event. A pre-processed and feature-extracted unknown audio signal is input to obtain frame-level prediction probabilities of all target sound events (TSEs), and sentence-level prediction probabilities of all categories of TSEs are obtained based on an optimal pooling strategy of each category of TSE.

Abstract: The present disclosure relates to systems and methods for image segmentation. The system may include at least one processor that is directed to obtain an image; divide the image into a plurality of image blocks; determine, for each of the plurality of image blocks, a compressed representation of one or more features of the image block; group the plurality of image blocks into at least two different categories based on the plurality of compressed representations; and extract a region of interest from the image based on at least one of the at least two different categories of the plurality of image blocks.

Abstract: The present disclosure relates to systems and methods for image segmentation. The system may include at least one processor that is directed to obtain an image; divide the image into a plurality of image blocks; determine, for each of the plurality of image blocks, a compressed representation of one or more features of the image block; group the plurality of image blocks into at least two different categories based on the plurality of compressed representations; and extract a region of interest from the image based on at least one of the at least two different categories of the plurality of image blocks.