Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification of birdsong recordings can be naturally formulated as a multiple instance problem, where bags of instances are represented by either features or dissimilarities. In bioacoustics, bags typically correspond to regions of interest in spectrograms, which are detected after a segmentation stage of the audio recordings. ,  use different dissimilarity measures beten bags and explore whether the subsequent application of metric learning/adaptation methods and the construction of dissimilarity spaces allow increasing the classification performance of birdsong recordings. A publicly available bioacoustic data set is used for the experiments. Our results suggest, in the first place, that appropriate dissimilarity measures are those which capture most of the overall differences beten bags, such as the modified Hausdorff distance and the mean minimum distance; in the second place, they confirm the benefit from adapting the applied dissimilarity measure as ll as the potential further enhancement of the classification performance by building dissimilarity spaces and increasing training set sizes.
['José Francisco Ruiz-Muñoz', 'Germán Castellanos-Domínguez', 'Mauricio Orozco-Alzate']
I am writing a research paper on a idea Enhancing the dissimilarity-based classification of birdsong recordings
[{"from": "human", "value": "I am writing a research paper on a idea Enhancing the dissimilarity-based classification of birdsong recordings"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification of birdsong recordings can be naturally formulated as a multiple instance problem, where bags of instances are represented by either features or dissimilarities. In bioacoustics, bags typically correspond to regions of interest in spectrograms, which are detected after a segmentation stage of the audio recordings. ,  use different dissimilarity measures beten bags and explore whether the subsequent application of metric learning/adaptation methods and the construction of dissimilarity spaces allow increasing the classification performance of birdsong recordings. A publicly available bioacoustic data set is used for the experiments. Our results suggest, in the first place, that appropriate dissimilarity measures are those which capture most of the overall differences beten bags, such as the modified Hausdorff distance and the mean minimum distance; in the second place, they confirm the benefit from adapting the applied dissimilarity measure as ll as the potential further enhancement of the classification performance by building dissimilarity spaces and increasing training set sizes."}]