Acoustic event diarization in TV/movie audios using deep embedding and integer linear programming
| Autor: | Mingle Liu, Jichen Yang, Yanxiong Li, Yuhan Zhang, Xianku Li, Wang Wucheng |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer Networks and Communications business.industry Computer science 020207 software engineering Pattern recognition Information bottleneck method 02 engineering and technology Spectral clustering Hierarchical clustering Hardware and Architecture Bayesian information criterion 0202 electrical engineering electronic engineering information engineering Media Technology Embedding Artificial intelligence Cluster analysis business Integer programming Software |
| Zdroj: | Multimedia Tools and Applications. 78:33999-34025 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-019-07991-6 |
| Popis: | In this study, we propose a method for acoustic event diarization based on a feature of deep embedding and a clustering algorithm of integer linear programming. The deep embedding learned by deep auto-encoder network is used to represent the properties of different classes of acoustic events, and then the integer linear programming is adopted for merging audio segments belonging to the same class of acoustic events. Four kinds of TV/movie audios (21.5 h in total) are used as experimental data, including Sport, Situation comedy, Award ceremony, and Action movie. We compare the deep embedding with state-of-the-art features. Further, the clustering algorithm of integer linear programming is compared with other clustering algorithms adopted in previous works. Finally, the proposed method is compared to both supervised and unsupervised methods on four kinds of TV/movie audios. The results show that the proposed method is superior to other unsupervised methods based on agglomerative information bottleneck, Bayesian information criterion and spectral clustering, and is little inferior to the supervised method based on deep neural network in terms of acoustic event error. |
| Databáze: | OpenAIRE |
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