Robust Audio Recognition Based on Gaussian Process Regression Model and Variational Auto-encoder
| Autor: | Min-Che Hsieh, 謝旻哲 |
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| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 The sense of hearing plays an important role in human’s daily life. In the case of hearing circumstances, sense of hearing not only enables people to understand the situation more clearly, but also enrich people’s life more colorful. Within all various of sound types, if we apply robust features and automated classification methods can assist us to understand different types of emergencies more quickly or enhance the effect of learning. Therefore, the classification of categories and robustness through ambient sound and musical instruments has gradually been taken more seriously. In the traditional auto-encoder, photos and audios are mainly reconstructed through the neural network [29], and it is conducive to connect all kinds of classifiers to enhance its recognition effect. On the other side, variational auto-encoder introduced random variational inference [25]. It uses the stochastic gradient method to re-parameterize the variational lower bound to achieve the best optimization results. Afterwards, they use the recognition model to estimate the more difficult the Posterior distribution. The Gaussian Process Regression Model is also required to derive the lower bound by training its parameters, and then we combine the lower bound of the variational auto-encoder and Gaussian process regression model. Finally, we train these parameters which including (Gaussian process regression model and the variational auto-encoder) will achieve the best optimize effect, by reducing the cost of time. In the experimental part, in order to show the robustness of this model, we compare the differences between noise and clean identification effect. And we will also discuss the differences between the initial parameters of different, to discover its speed of convergence and identification effect. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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