In this paper, we propose a Correlation-Maximization denoising filter which utilizes periodicity information to remove additive noise in bird calls. We also developed a statistically-based noise robust bird-call classification system which uses the denoising filter as a frontend. Enhanced bird calls which are the output of the denoising filter are used for feature extraction. Gaussian Mixture M...

This paper presents a description of the INESC-ID Spoken Language Systems Laboratory (L2F) Age and Gender classification system submitted to the INTERSPEECH 2010 Paralinguistic Challenge. The L2F Age classification system and the Gender classification system are composed respectively by the fusion of four and six individual sub-systems trained with short and long term acoustic and prosodic feat...

In this letter, we propose an efficient method to improve the performance of voiced/unvoiced (V/UV) sounds decision for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). We first present an effective analysis of the features and the classification method adopted in the SMV. And feature vectors which are applied to the GMM are then selected from relevant paramete...

Condition monitoring techniques are described in this chapter. Two aspects of condition monitoring process are considered: (1) feature extraction; and (2) condition classification. Feature extraction methods described and implemented are fractals, Kurtosis and Melfrequency Cepstral Coefficients. Classification methods described and implemented are support vector machines (SVM), hidden Markov mo...

We consider Gaussian mixture model (GMM)-based classification from noisy features, where the uncertainty over each feature is represented by a Gaussian distribution. For that purpose, we first propose a new GMM training and decoding criterion called log-likelihood integration which, as opposed to the conventional likelihood integration criterion, does not rely on any assumption regarding the di...

In this paper, a class of GMM-based discriminative kernels is proposed for speaker identification. We map an utterance vector into a matrix by finding the sequence of components, which have the maximum likelihood in the GMM for the all frame vectors. And the weights matrix was used, which were got by the GMM's parameters. Then the SVMs are used for classification. A one-versus-rest fashion is u...

In this paper we aim to improve the performance of Gaussian Mixture Model (GMM) classifier using Impostor model parameters for a closed set Speaker Identification task. We propose a novel method of speaker model training which uses the parameters of an Impostor Model to discriminatively train, in order to improve the performance of the GMM based classifier. This is unlike conventional technique...

In this paper, we discuss the issues in automatic recognition of vowels in Persian language. The present work focuses on new statistical method of recognition of vowels as a basic unit of syllables. First we describe a vowel detection system then briefly discuss how the detected vowels can feed to recognition unit. According to pattern recognition, Support Vector Machines (SVM) as a discriminat...

This paper investigates a generalized method of moments (GMM) approach to the estimation of autoregressive roots near unity with panel data and incidental deterministic trends. Such models arise in empirical econometric studies of Þrm size and in dynamic panel data modeling with weak instruments. The two moment conditions in the GMM approach are obtained by constructing bias corrections to the ...

In this paper, we re-examine the empirical relevance of the cost channel of monetary policy. We employ recently developed moment-conditions inference procedures, which provide a more e¢ cient and reliable econometric framework than in previous literature. Using US data, our results suggest that there is no substantial evidence for the existence of a cost channel. Keywords: Cost channel; Phillip...