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Title Pitch based sound classification
Author Nielsen, Andreas Brinch (Intelligent Signal Processing, Informatics and Mathematical Modelling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Supervisor Hansen, Lars Kai (Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2005
Abstract The fact that different sound environments need different sound processing is no secret, but how to select between the different programs is very different from hearing aid to hearing aid. Complete automatic and reliable classification is desirable, because many hearing aid users are not able to select programs themselves. In this project the emphasis is on classification based on the pitch of the signal, and three classes, music, noise and speech, is used. Unfortunately pitch is not straightforward to extract, and the first part of the project is about finding a suitable pitch detector. A new pitch detector is suggested based on two existing algorithms, pattern match with envelope detection and the harmonic product spectrum. The new algorithm is compared to a Bayesian algorithm and HMUSIC, and is found to perform better for classification purposes. Features are extracted from the signal produced by the pitch detector. Apart from the pitch itself, the error from the pitch detector is used to get a measure of how well the extracted pitch describes the signal, i.e. whether the signal is pitched or not. A total of 28 features, some overlapping, are suggested. A model is set up for classification to evaluate the features found. The Bayes classifier is used and during training an interesting property is discovered. The training error increases for high numbers of features. Maximum likelihood estimations should always result in decreasing training error for increasing dimensions of the model. The explanation is that the Bayes classifier is not trained for classification, but for the within class likelihood. When the data is not distributed like the model, it does not result in maximum likelihood in classification. A new model that ensures maximum likelihood in classification is suggested and compared to a generative and a discriminative model. A better performance than the generative and comparable to the discriminative is obtained. Finally a model, using the new model and 5 features, is suggested. The validation classification error of this model is only 1.9 %. The influence of the pitch detector’s precision on the classification is investigated. The error is clearly increasing for worse precision, but very little seems to be gained for higher precision than already used.
Imprint Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU : DK-2800 Kgs. Lyngby, Denmark
Pages 126
Keywords Pitch detection; HPS; HMUSIC; feature extraction; classification; sound; music; noise; speech; Bayes; generative; discriminative
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Admin Creation date: 2006-06-22    Update date: 2012-12-17    Source: dtu    ID: 185911    Original MXD