Äänisisällön automaattisen luokittelun menetelmiä

This study presents an overview of different methods of digital signal processing and pattern recognition that are frequently applicable to automatic recognition, classification and description of audio content. Moreover, strategies for the combination of the said methods are discussed. Some of the published practical applications from different areas are cited to illustrate the use of the basic methods and the combined recognition strategies. A brief overview of human auditory perception is also given, with emphasis on the aspects that are important for audio recognition.Tässä työssä esitetään yleiskatsaus sellaisiin signaalinkäsittelyn ja hahmontunnistuksen menetelmiin, jotka ovat usein sovellettavissa äänisisällön automaattiseen tunnistamiseen, luokitteluun ja kuvaamiseen. Lisäksi työssä esitetään strategioita mainittujen menetelmien yhdistelyyn ja annetaan näihin ratkaisuihin liittyviä esimerkinomaisia viitteitä kirjallisuudesta löytyviin käytännön sovelluksiin eri sovellusalueilta. Työ sisältää myös suppean esityksen ihmisen kuulon toiminnan pääpiirteistä äänitunnistuksen kannalta

Word-Final /s/ in English

Synopsis: The complexities of speech production, perception, and comprehension are enormous. Theoretical approaches of these complexities most recently face the challenge of accounting for findings on subphonemic differences. The aim of the present dissertation is to establish a robust foundation of findings on such subphonemic differences. One rather popular case for differences in subphonemic detail is word-final /s/ and /z/ in English (henceforth S) as it constitutes a number of morphological functions. Using word-final S, three general issues are investigated. First, are there subphonemic durational differences between different types of word-final S? If there are such differences, how can they be accounted for? Second, can such subphonemic durational differences be perceived? Third, do such subphonemic durational differences influence the comprehension of S? These questions are investigated by five highly controlled studies: a production task, an implementation of Linear Discriminative Learning, a same-different task, and two number-decision tasks. Using not only real words but also pseudowords as target items, potentially confounding effects of lexical storage are controlled for. Concerning the first issue, the results show that there are indeed durational differences between different types of word-final S. Non-morphemic S is longest in duration, clitic S is shortest in duration, and plural S duration is in-between non-morphemic S and clitic S durations. It appears that the durational differences are connected to a word’s semantic activation diversity and its phonological certainty. Regarding the second issue, subphonemic durational differences in word-final S can be perceived, with higher levels of perceptibility for differences of 35 ms and higher. In regard to the third issue, subphonemic durational differences are found not to influence the speed of comprehension, but show a significant effect on the process of comprehension. The overall results give raise to a revision of various extant models of speech production, perception, and comprehension

Production, perception, and comprehension of subphonemic detail

The complexities of speech production, perception, and comprehension are enormous. Theoretical approaches of these complexities most recently face the challenge of accounting for findings on subphonemic differences. The aim of the present dissertation is to establish a robust foundation of findings on such subphonemic differences. One rather popular case for differences in subphonemic detail is word-final /s/ and /z/ in English (henceforth S) as it constitutes a number of morphological functions. Using word-final S, three general issues are investigated. First, are there subphonemic durational differences between different types of word-final S? If there are such differences, how can they be accounted for? Second, can such subphonemic durational differences be perceived? Third, do such subphonemic durational differences influence the comprehension of S? These questions are investigated by five highly controlled studies: a production task, an implementation of Linear Discriminative Learning, a same-different task, and two number-decision tasks. Using not only real words but also pseudowords as target items, potentially confounding effects of lexical storage are controlled for. Concerning the first issue, the results show that there are indeed durational differences between different types of word-final S. Non-morphemic S is longest in duration, clitic S is shortest in duration, and plural S duration is in-between non-morphemic S and clitic S durations. It appears that the durational differences are connected to a word’s semantic activation diversity and its phonological certainty. Regarding the second issue, subphonemic durational differences in word-final S can be perceived, with higher levels of perceptibility for differences of 35 ms and higher. In regard to the third issue, subphonemic durational differences are found not to influence the speed of comprehension, but show a significant effect on the process of comprehension. The overall results give raise to a revision of various extant models of speech production, perception, and comprehension

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform