Convolutional Neural Network and Feature Transformation for Distant Speech Recognition

Pardede, Hilman F.; Yuliani, Asri Rizki; Sustika, Rika

doi:10.11591/ijece.v8i6.pp5381-5388

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…maxpooling layer, a fully connected layer (FC), are connected to each neuron of that layer. The output would be the number of generated classes on that classification for that layer [29,13].…”

Section: Materials and Methods 21 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Deep image mining for convolution neural network

Jasm

Hamad

Alrawi

2020

IJEECS

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Image mining is the method of searching and discovering valuable information and knowledge from a huge image dataset. Image mining is based on data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining handled with the hidden information extraction, an association of image data and additional pattern which are not clearly visible in the image. Choosing the proper objects or the feature of the image to be suitable for image mining process is the main challenge would face the programmer. The process includes fine out the most efficient routes at a shorter time and saving the users effort. The main objective of this paper is to design and implement the image classification system with a higher performance, where a CIFAR-10 data set is used to train and testing classification models using CNN. A convolutional neural network is trustworthy, and it could lead to high-quality results. The high accuracy of 98% has been obtained using deep convolutional neural network (DCNN).

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Section: Materials and Methods 21 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Deep image mining for convolution neural network

Jasm

Hamad

Alrawi

2020

IJEECS

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“…Neural network forward is selected to generate bits codeword M+F from bits source M because it has same function of matrix generating in encoder traditional [15][16][17]. Neural network forward also known as neural network generating difference from neural network normal by some specific such as SNN,SOFM,etc.…”

Section: Encoder Neural Network Mechanismmentioning

confidence: 99%

Transceiver error reduction by design prototype system based on neural network analysis method

Fadhil

Naji

Salman

2020

IJEECS

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Code words traditional can be decoding when applied in artificial neural network. Nevertheless, explored rarely for encoding of artificial neural network so that it proposed encoder for artificial neural network forward with major structure built by Self Organizing Feature Map (SOFM). According to number of bits codeword and bits source mentioned the dimension of forward neural network at first then sets weight of distribution proposal choosing after that algorithm appropriate using for sets weight initializing and finally sets code word uniqueness check so that matching with existing. The spiking neural network (SNN) using as decoder of neural network for processing of decoding where depending on numbers of bits codeword and bits source dimension the spiking neural network structure built at first then generated sets codeword by network neural forward using for train spiking neural network after that when whole error reached minimum the process training stop and at last sets code word decode accepted. In tests simulation appear that feasible decoding and encoding neural network while performance better for structure network neural forward a proper condition is achieved with γ node output degree. The methods of mathematical traditional can not using for decoding generated Sets codeword by encoder network of neural so it is prospect good for communication security.

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“…The convolutional model contains several convolutional and pooling layers which provide high level representation of sentence vector. Convolutional Neural Network (CNN) is a special kind of deep neural network [24]. a. Convolution From the input sentence matrix Y, the feature map parameters (e (v,j) , d (v,j) ) are computed in the convolutional layer and convolutional filter .…”

Section: Semantic Matching Patternmentioning

confidence: 99%

Convolutional recurrent neural network with template based representation for complex question answering

Reddy¹,

Madhavi²

2020

IJECE

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Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.

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Convolutional Neural Network and Feature Transformation for Distant Speech Recognition

Cited by 8 publications

References 25 publications

Deep image mining for convolution neural network

Deep image mining for convolution neural network

Transceiver error reduction by design prototype system based on neural network analysis method

Convolutional recurrent neural network with template based representation for complex question answering

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