Development of learned dictionary based spoken language
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This progress report summarizes the work done so far on developing a dictionary learning-based spoken language identification system. It discusses the objectives of designing efficient dictionary learning algorithms and extracting suitable parameters. It reviews literature on different dictionary learning algorithms like K-SVD, LC-KSVD, D-KSVD, and Discriminative Bayesian dictionary learning. The report explains the working of these algorithms and highlights progress made in implementing MATLAB code for an existing algorithm and extracting/analyzing parameters to compare algorithm outputs and identify the most efficient one. Future work involves completing the MATLAB code and experiments.
![Fig. 1:Bayesian Dictionary Learning Process[7]](https://image.slidesharecdn.com/developmentoflearneddictionarybasedspokenlanguage-161203154155/85/Development-of-learned-dictionary-based-spoken-language-10-320.jpg)
![References:
[1] K. Engan, S. Aase, and J. Husφy. Frame based signal compression using method
of optimal directions (mod), 1999. IEEE Intern. Symp. Circ. Syst., 1999.
[2] D. Pham and S. Venkatesh. Joint learning and dictionary construction for
pattern recognition, 2008. CVPR.
[3] Q. Zhang and B. Li. Discriminative k-svd for dictionary learning in face
recognition, 2010. CVPR.
[4] "Discriminative Bayesian Dictionary Learning for Classification", IEEE
Transactions on Pattern Analysis & Machine Intelligence, , no. 1, pp. 1, PrePrints
PrePrints, doi:10.1109/TPAMI.2016.2527652](https://image.slidesharecdn.com/developmentoflearneddictionarybasedspokenlanguage-161203154155/85/Development-of-learned-dictionary-based-spoken-language-12-320.jpg)
Development of learned dictionary based spoken language
- 1. MINI PROJECT PROGRESS REPORT ON “DEVELOPMENT OF LEARNED DICTIONARY BASED SPOKEN LANGUAGE IDENTIFICATION SYSTEM” Under the guidance of Mr. Om Prakash Singh Associate Professor ECE Dept., SMIT, Majitar. Presented By: PALLAVI BHARTI (20130549) 7thsem. RICHA BHARTI (20130707) 7thsem.
- 2. Objective: Design and Analysis of Efficient Algorithms for Dictionary learning. Extraction of suitable parameters for efficient calculation. Mathematical modelling of an efficient algorithm for dictionary learning.
- 3. Planning: Perform statistical analysis of pre-existing algorithm. Develop MATLAB code for algorithm design application. Design of algorithm for language identification.
- 4. Progress: Literature Survey about: 1. What is dictionary learning? 2. Different types of Algorithms for dictionary learning. K-SVD. LC-KSVD. D-KSVD. Discriminative Bayesian dictionary learning.
- 5. What is Dictionary learning? Dictionary learning is compact representation of training data or test data .it is the best way to represent a signal. Dictionary learning is a branch of signal processing that aims at finding a frame (called dictionary) in which some training data admits sparse representation.
- 6. K-SVD Algorithm: 1. K-means clustering process for adapting dictionaries in order to achieve sparse signal representation. 2. Application that can benefit from the sparsity and over completeness concepts include compression and feature extraction. 3. In order to over complete and sparse representation the simple technique is Orthogonal matching pursuit(OMP).
- 7. D-KSVD Algorithm: 1. Discriminative K-SVD (D-KSVD), a newly proposed dictionary learning method, has better discrimination ability since it incorporates the classification error into its object function and learns a discriminative dictionary and a linear classifier . 2. D-KSVD is a two-step iterative method that is intialiation and classification, and its convergence speed is heavily influenced by the initialization values, initialization method is proposed for the D-KSVD dictionary learning algorithm. 3 Naive Bayesian classifier is used to initialize the linear classifier in D- KSVD.
- 8. LC-KSVD Algorithm: 1. A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. 2. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. 3. Algorithm learns a single over-complete dictionary and an optimal linear classifier jointly. It yields dictionaries so that feature points with the same class labels have similar sparse codes.
- 9. Discriminative Bayesian dictionary learning: 1. The proposed approach infers the probablity distribuition over the atoms of a discriminative dictionary using a finite approximation of beta process. 2. It also computes sets of bernoulli distribuition that associate a class labels to the learned dictionary atoms. 3. Test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier
- 10. Fig. 1:Bayesian Dictionary Learning Process[7]
- 11. Jobs to be done: Implementation of MATLAB code for pre-existing algorithm. Study of the corresponding pre-existing algorithms. Extraction of the parameters and then observation and analysis for output. The result will be compared and plotted in the code set prepared in MATLAB 2011. Checking and comparison of the output for most efficient algorithm design.
- 12. References: [1] K. Engan, S. Aase, and J. Husφy. Frame based signal compression using method of optimal directions (mod), 1999. IEEE Intern. Symp. Circ. Syst., 1999. [2] D. Pham and S. Venkatesh. Joint learning and dictionary construction for pattern recognition, 2008. CVPR. [3] Q. Zhang and B. Li. Discriminative k-svd for dictionary learning in face recognition, 2010. CVPR. [4] "Discriminative Bayesian Dictionary Learning for Classification", IEEE Transactions on Pattern Analysis & Machine Intelligence, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TPAMI.2016.2527652
- 13. THANK YOU