Name
Abstract | ||
|---|---|---|
Analysis and recognition of auditory scenes play an important role in content-based multimedia processing and context-aware applications. In this paper, we propose an auditory scene recognition scheme that integrates the analysis of the audio data of scene with LDA topic model to discover latent structures (i.e. contextual correlations) of audio words, and generation of intermediate contextual descriptions of audio data on basis of the topics learnt by LDA. We further combine the piecewise low-level audio feature and the contextual feature, and discriminatively classify an audio clip of an unknown scene that is represented as a set of these features using the Hough forest model. The experimental results demonstrate the effectiveness of the proposed scheme, which combines the unsupervised topic modeling by LDA and the supervised classification of auditory scene by Hough forest. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICME.2014.6890241 | ICME |
Keywords | Field | DocType |
environmental sound,piecewise low-level audio feature,auditory scene analysis,contextual feature,local discriminant bases,audio clip classification,auditory scene recognition,audio words,intermediate contextual description generation,supervised classification,lda,hough forest,signal classification,content-based multimedia processing,auditory scene,audio signal processing,unsupervised topic modeling,hough forest model,context-aware applications,lda topic model,latent structure discovery,mel frequency cepstral coefficient,correlation,context modeling,hidden markov models,accuracy,feature extraction | Mel-frequency cepstrum,Computer vision,Auditory scene analysis,Pattern recognition,Computer science,Feature extraction,Context model,Speech recognition,Artificial intelligence,Topic model,Hidden Markov model,Piecewise | Conference |
ISSN | Citations | PageRank |
1945-7871 | 2 | 0.42 |
References | Authors | |
10 | 1 | |