排序方式: 共有7条查询结果,搜索用时 15 毫秒
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Mehdi Mohammadi Bijan Raahemi Ahmad Akbari Hossein Moeinzadeh Babak Nasersharif 《Expert systems with applications》2011,38(6):6417-6423
In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does. 相似文献
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Behzad Zamani Ahmad Akbari Babak Nasersharif Azarakhsh Jalalvand 《Pattern recognition letters》2011,32(7):948-955
Feature extraction is an important component of pattern classification and speech recognition. Extracted features should discriminate classes from each other while being robust to environmental conditions such as noise. For this purpose, several feature transformations are proposed which can be divided into two main categories: data-dependent transformation and classifier-dependent transformation. The drawback of data-dependent transformation is that its optimization criteria are different from the measure of classification error which can potentially degrade the classifier’s performance. In this paper, we propose a framework to optimize data-dependent feature transformations such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis) and HLDA (Heteroscedastic LDA) using minimum classification error (MCE) as the main objective. The classifier itself is based on Hidden Markov Model (HMM). In our proposed HMM minimum classification error technique, the transformation matrices are modified to minimize the classification error for the mapped features, and the dimension of the feature vector is not changed. To evaluate the proposed methods, we conducted several experiments on the TIMIT phone recognition and the Aurora2 isolated word recognition tasks. The experimental results show that the proposed methods improve performance of PCA, LDA and HLDA transformation for mapping Mel-frequency cepstral coefficients (MFCC). 相似文献
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Naderi Navid Nasersharif Babak Nikoofard Amirhossein 《Multimedia Tools and Applications》2022,81(3):3629-3645
Multimedia Tools and Applications - An end-to-end text-to-speech system generates acoustic features directly from input text to synthesize speech from it. The challenges of using these models for... 相似文献
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Mehralian Soheil Teshnehlab Mohammad Nasersharif Babak 《Pattern Analysis & Applications》2021,24(4):1699-1711
Pattern Analysis and Applications - In many machine learning applications and algorithms, the algorithm performance and accuracy are highly dependent on the metric used to measure the distance... 相似文献
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Shima Tabibian Ahmad Akbari Babak Nasersharif 《Engineering Applications of Artificial Intelligence》2013,26(7):1660-1670
Keyword spotting refers to detection of all occurrences of any given keyword in input speech utterances. In this paper, we define a keyword spotter as a binary classifier that separates a class of sentences containing a target keyword from a class of sentences which do not include the target keyword. In order to discriminate the mentioned classes, an efficient classification method and a suitable feature set are to be studied. For the classification method, we propose an evolutionary algorithm to train the separating hyper-plane between the two classes. As our discriminative feature set, we propose two confidence measure functions. The first confidence measure function computes the possibility of phonemes presence in the speech frames, and the second one determines the duration of each phoneme. We define these functions based on the acoustic, spectral and statistical features of speech. The results on TIMIT indicate that the proposed evolutionary-based discriminative keyword spotter has lower computational complexity and higher speed in both test and train phases, in comparison to the SVM-based discriminative keyword spotter. Additionally, the proposed system is robust in noisy conditions. 相似文献
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A keyword spotter is considered as a binary classifier that separates a class of utterances containing a target keyword from utterances without the keyword. These two classes are not inherently linearly separable. Thus, linear classifiers are not completely suitable for such cases. In this paper, we extend a kernel-based classification approach to separate the mentioned two non-linearly separable classes so that the area under the Receiver/Relative Operating Characteristic (ROC) curve (the most common measure for keyword spotter evaluation) is maximized. We evaluated the proposed keyword spotter under different experimental conditions on TIMIT database. The results indicate that, in false alarm per keyword per hour smaller than two, the true detection rate of the proposed kernel-based classification approach is about 15 % greater than that of the linear classifiers exploited in previous researches. Additionally, area under the ROC curve (AUC) of the proposed method is 1 % higher than AUC of the linear classifiers that is significant due to confidence levels 80 and 95 % obtained by t-test and F-test evaluations, respectively. In addition, we evaluated the proposed method in different noisy conditions. The results indicate that the proposed method show a good robustness in noisy conditions. 相似文献
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Rashno Elyas Akbari Ahmad Nasersharif Babak 《Neural computing & applications》2022,34(19):16753-16769
Neural Computing and Applications - The performance of convolutional neural networks is degraded by noisy data, especially in the test phase. To address this challenge, a new convolutional neural... 相似文献
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