The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Sang Keun Choe - One of the best experts on this subject based on the ideXlab platform.
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Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Sungkyun Chang, Sang Keun ChoeAbstract:In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output-the cover-probabilities of one song to all other candidate songs-as a new Representation Vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the Representation Vectors; 2. the cosine distance between the Representation Vectors; and 3. the correlation between the Vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
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ICASSP - Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Sungkyun Chang, Sang Keun ChoeAbstract:In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new Representation Vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the Representation Vectors; 2. the cosine distance between the Representation Vectors; and 3. the correlation between the Vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
Sungkyun Chang - One of the best experts on this subject based on the ideXlab platform.
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Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Sungkyun Chang, Sang Keun ChoeAbstract:In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output-the cover-probabilities of one song to all other candidate songs-as a new Representation Vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the Representation Vectors; 2. the cosine distance between the Representation Vectors; and 3. the correlation between the Vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
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ICASSP - Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Sungkyun Chang, Sang Keun ChoeAbstract:In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new Representation Vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the Representation Vectors; 2. the cosine distance between the Representation Vectors; and 3. the correlation between the Vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
Monther Aldwairi - One of the best experts on this subject based on the ideXlab platform.
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Authors' Writing Styles Based Authorship Identification System Using the Text Representation Vector
2019 16th International Multi-Conference on Systems Signals & Devices (SSD), 2019Co-Authors: Nacer Eddine Benzebouchi, Nabiha Azizi, Nacer Eddine Hammami, Didier Schwab, Mohammed Chiheb Eddine Khelaifia, Monther AldwairiAbstract:Text mining is one of the main and typical tasks of machine learning (ML). Authorship identification (AI) is a standard research subject in text mining and natural language processing (NLP) that has undergone a remarkable evolution these last years. We need to identify/determine the actual author of anonymous texts given on the basis of a set of writing samples. Standard text classification often focuses on many handcrafted features such as dictionaries, knowledge bases, and different stylometric characteristics, which often leads to remarkable dimensionality. Unlike traditional approaches, this paper suggests an authorship identification approach based on automatic feature engineering using word2vec word embeddings, taking into account each author's writing style. This system includes two learning phases, the first stage aims to generate the semantic Representation of each author by using word2vec to learn and extract the most relevant characteristics of the raw document. The second stage is to apply the multilayer perceptron (MLP) classifier to fix the classification rules using the backpropagation learning algorithm. Experiments show that MLP classifier with word2vec model earns an accuracy of 95.83% for an English corpus, suggesting that the word2vec word embedding model can evidently enhance the identification accuracy compared to other classical models such as n-gram frequencies and bag of words.
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SSD - Authors' Writing Styles Based Authorship Identification System Using the Text Representation Vector
2019 16th International Multi-Conference on Systems Signals & Devices (SSD), 2019Co-Authors: Nacer Eddine Benzebouchi, Nabiha Azizi, Nacer Eddine Hammami, Didier Schwab, Mohammed Chiheb Eddine Khelaifia, Monther AldwairiAbstract:Text mining is one of the main and typical tasks of machine learning (ML). Authorship identification (AI) is a standard research subject in text mining and natural language processing (NLP) that has undergone a remarkable evolution these last years. We need to identify/determine the actual author of anonymous texts given on the basis of a set of writing samples. Standard text classification often focuses on many handcrafted features such as dictionaries, knowledge bases, and different stylometric characteristics, which often leads to remarkable dimensionality. Unlike traditional approaches, this paper suggests an authorship identification approach based on automatic feature engineering using word2vec word embeddings, taking into account each author's writing style. This system includes two learning phases, the first stage aims to generate the semantic Representation of each author by using word2vec to learn and extract the most relevant characteristics of the raw document. The second stage is to apply the multilayer perceptron (MLP) classifier to fix the classification rules using the backpropagation learning algorithm. Experiments show that MLP classifier with word2vec model earns an accuracy of 95.83% for an English corpus, suggesting that the word2vec word embedding model can evidently enhance the identification accuracy compared to other classical models such as n-gram frequencies and bag of words.
Yong Rui - One of the best experts on this subject based on the ideXlab platform.
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Learning word Representation considering proximity and ambiguity
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014Co-Authors: Lin Qiu, Zaiqing Nie, Yong Cao, Yong Yu, Yong RuiAbstract:Distributed Representations of words (aka word embedding) have proven helpful in solving natural language processing (NLP) tasks. Training distributed Representations of words with neural networks has lately been a major focus of re-searchers in the field. Recent work on word embedding, the Continuous Bag-of-Words (CBOW) model and the Contin-uous Skip-gram (Skip-gram) model, have produced particu-larly impressive results, significantly speeding up the training process to enable word Representation learning from large-scale data. However, both CBOW and Skip-gram do not pay enough attention to word proximity in terms of model or word ambiguity in terms of linguistics. In this paper, we propose Proximity-Ambiguity Sensitive (PAS) models (i.e. PAS CBOW and PAS Skip-gram) to produce high quality distributed Representations of words considering both word proximity and ambiguity. From the model perspective, we in-troduce proximity weights as parameters to be learned in PAS CBOW and used in PAS Skip-gram. By better modeling word proximity, we reveal the strength of pooling-structured neu-ral networks in word Representation learning. The proximity-sensitive pooling layer can also be applied to other neural net-work applications that employ pooling layers. From the lin-guistics perspective, we train multiple Representation Vectors per word. Each Representation Vector corresponds to a partic-ular group of POS tags of the word. By using PAS models, we achieved a 16.9% increase in accuracy over state-of-the-art models.
W.b. Mikhael - One of the best experts on this subject based on the ideXlab platform.
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ISCAS (4) - A novel adaptive algorithm applied to a class of redundant Representation Vector quantizers for waveform and model based coding
2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353), 2002Co-Authors: V. Krishnan, W.b. MikhaelAbstract:Recently, novel Vector quantization techniques in multiple nonorthogonal domains for both waveform and Linear Prediction (LP) model based, signal characterization have been reported. This approach gives an improved signal coding performance as compared to Vector quantization in a single domain. In these techniques, each Vector, formed either directly from the signal waveform or from the LP model coefficients extracted from the signal, is encoded in the domain that best represents the Vector. An iterative algorithm for codebook accuracy enhancement, applicable to both waveform and LP model based Vector quantization in nonorthogonal domains is developed and presented in this paper. In this algorithm, in the learning mode, each set of codebooks is retrained by those training Vectors that selected that particular set of codebooks in the most recent iteration. Sample results are provided which clearly demonstrate the improved performance for the same bitrate.
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A novel adaptive algorithm applied to a class of redundant Representation Vector quantizers for waveform and model based coding
2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353), 2002Co-Authors: V. Krishnan, W.b. MikhaelAbstract:Recently, novel Vector quantization techniques in multiple nonorthogonal domains for both waveform and Linear Prediction (LP) model based, signal characterization have been reported. This approach gives an improved signal coding performance as compared to Vector quantization in a single domain. In these techniques, each Vector, formed either directly from the signal waveform or from the LP model coefficients extracted from the signal, is encoded in the domain that best represents the Vector. An iterative algorithm for codebook accuracy enhancement, applicable to both waveform and LP model based Vector quantization in nonorthogonal domains is developed and presented in this paper. In this algorithm, in the learning mode, each set of codebooks is retrained by those training Vectors that selected that particular set of codebooks in the most recent iteration. Sample results are provided which clearly demonstrate the improved performance for the same bitrate.
