dc.description.abstract | Sound recognition applications play an important role in various aspects of human life, with research efforts being put into recognition systems of different kinds of sounds, i.e. speech, music, and environmental sounds. This thesis deals with the problem of environmental sound recognition, as it is a highly interesting part of sound recognition research due to the range of potential applications that benefit from it. We address two prominent parts of a recognition problem that hold an important role in delivering high performance in terms of recognition accuracy, i.e. the feature extraction and classification part.
We proposed to use features extracted from the wavelet domain of a signal, as it is considered to provide better analysis of environmental sound audio signals. We extract the wavelet packet decomposition of an audio signal, and derive the signal’s spectral centroid, sparsity, flatness and spread using the wavelet nodes, as well as a set of wavelet-based cepstral coefficients. In addition, we propose the use of a set of histogram features calculated from the wavelet based features. We compare the performance of the different feature sets in our experiments.
In the classification part of the system, we propose the use of Gaussian Process based classifier. We propose a multiple kernel approach, in which we combined the linear kernal and probability product kernel to present two different kinds of similarity notion from our data in the learning algorithm. We show the probability product kernel between two kernel density estimations, and then combine it with the linear kernel using a weighted linear combination approach, and multiplication approach.
Two kinds of recognition problems are observed in this thesis, i.e. singular and multi-label problems. Through our experiments, we show that the proposed features and classification approach yielded satisfying recognition results in both singular and multi-label classification. Moreover, the use of multiple features in multiple kernel in a Gaussian Process further improved the system performance. | zh_TW |