| dc.description.abstract | In recent years, environmental sound recognition has become a new research topic in home automation. In home automation systems, the sound recognized by the system becomes the basis for performing certain tasks. For a recognition system, features and classifiers play the important roles in improving performance. This thesis adapts the nonuniform scale-frequency maps (nSFMs) as the feature, and the Gaussian process is chosen as the classifier. However, apart from features and classifiers, the reliability of the data should be also taken into consideration. Therefore, we propose a new confidence estimation approach to achieve the outlier detection. Two confidence measures called data confidence and dimension confidence are defined. And two relative kernels are proposed for the Gaussian process. A threshold is set to decide whether the data point is an outlier or not. If the confidence value of the data point is less than the threshold, the data point is regarded as an outlier. Otherwise, it is a normal data.
For the dictionary selection, the matrix factorization based dictionaries are discussed, such as standard nonnegative matrix factorization (NMF), Semi-NMF, sparse NMF, principal component analysis (PCA), and 2D (Semi-)NMF. Experiments are conducted on a 20 class environmental sound database. The results indicate that the confidence values estimated by the sparse NMF dictionary are discriminative and have better performances in the proposed outlier detection approach.
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