Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification

Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abu...

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Bibliographic Details
Main Authors: Seongkyu Mun, Suwon Shon, Wooil Kim, Han, David K., Hanseok Ko
Format: Conference Proceeding
Language:English
Subjects:
acoustic scene classification
Acoustics
Conferences
Convolution
deep neural network
mid-level feature
Neural networks
Speech recognition
Surveillance
Training
Transfer learning
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Summary:Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for training Acoustic Scene Classification (ASC) models. By exploiting VOC DNN's ability of learning beyond its pre-trained environments, this paper proposes DNN based transfer learning for ASC. Effectiveness of the proposed method is demonstrated on the database of IEEE DCASE Challenge 2016 Task 1 and home surveillance environment via representative experiments. Its improved performance is verified by comparing it to prominent conventional methods.
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7952265