Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification

Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification

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Acoustic scene classification (ASC) tries to inference information about the environment using audio read more segments.The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar.In this paper, the similarity relations amongst scenes are correlated with the classification error.

A class hierarchy construction method by using classification error is then proposed and integrated into a multitask learning framework.The experiments have shown that the proposed multitask learning method improves the performance of ASC.On the TUT Acoustic Scene 2017 dataset, we obtain the ensemble fine-grained accuracy of 81.

4%, which is better Cushion than the state-of-the-art.By using multitask learning, the basic Convolutional Neural Network (CNN) model can be improved by about 2.0 to 3.

5 percent according to different spectrograms.The coarse category accuracies (for two to six super-classes) range from 77.0% to 96.

2% by single models.On the revised version of the LITIS Rouen dataset, we achieve the ensemble fine-grained accuracy of 83.9%.

The multitask learning models obtain an improvement of 1.6% to 1.8% compared to their basic models.

The coarse category accuracies range from 94.9% to 97.9% for two to six super-classes with single models.