Informatics Publishing Limited

Kallakunta Ravi Kumar ¹, L. Bhavani ¹, P. Spandana ¹, K. Rishitha ¹, G. Ashitha ¹

Affiliations
1 Department of ECM, K. L. University, Vaddeswaram, Guntur, Andhra Pradesh, IN

Abstract

Background/Objectives: The performance of the speech enhancement techniques is examined by applying them to the speech signals corrupted by real world noise. Methods: An advanced coding Methodology is introduced to reduce the noisy signal using H-infinity filter. Here two compression techniques - Switch Split Vector Quantization (SSVQ) and Multi Stage Vector Quantization (MSVQ) are used. The enhancement techniques like Kalman Filter and Recursive Filter were assessed for similarities and differences with H-Infinity Filter and the outcomes are compared using signal to noise ratio which is likely to be affected on real world noise. Findings: In this paper using kalman filter, recursive filter, h-infinity filter plays vital role for comparing the various parameter characteristics. Kalman filter uses ordered steps that solve a mathematical problem. H-infinity filter differs from the normal changed Kalman filtering as it requires the knowledge of commotion parameters. H-infinity minimizes the estimation errors and thus obtains robustness and obtains the better results in enhancement. Application/Improvements: This study can be helpful for improving the intelligibility in speech signal and the same method can be implemented with different types of hybrid vector quantization techniques and may achieve better SNR and quality of the signal.

Keywords

Commotion, Multi Stage Vector Quantization (MSVQ), Real World Noise, Signal-to-Noise Ratio (SNR), Switch Split Vector Quantization (SSVQ).

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Kallakunta Ravi Kumar ¹, V. Bhanu Prakash ¹, V. Shyam ¹, M. Anil Kumar ¹

Affiliations
1 Department of Electronics and Computer Engineering, K L University, Guntur - 522502, Andhra Pradesh, IN

Abstract

Objectives: To identify the Objects individually in an image. Methods/Statistical Analysis: The texture based object detection is done based on the texture available over the surface of the object and the shape based object detection is done based on the outline of the objects in an image. These two methods are used for the object detection in an image. Findings: Both of the methods prove to have their own advantages and limitations; so based on the applications the appropriate method can be applied. Applications: Biometric recognition, surveillance, Medical Analysis.

Keywords

Object Detection, Shape Detection, Shape Contexts, Shape Recognition and Detection, Texture.

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Kallakunta Ravi Kumar ¹, K. R. Praneeth ¹, Sd. Rumani ¹, A. N. S. Lavanyamani ¹

Affiliations
1 Department of Electronics and Computer Engineering, K L University, Guntur - 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: Face recognition has become a challenging and fast growing area in real time applications. The paper deals with recognition and extracts the single person face from the group photograph. It becomes the next generation technology. Methods/Statistical Analysis: skin detection algorithms is performed to recognize the face and extract the features. Grey Level Co-occurrences Matrix Technique were used to analyse the feature. Findings: We proposed a system for face recognition and detection. Face is extracted from the input image using detection algorithm. Binary distance helps to locate the bounding box limits. GLCM technique helps to extract the grey scale properties from the input image. These properties are compared to the database to identify the person. Application/Improvements: The accuracy rates can further be improved by considering various factors like pose, expressions etc.

Keywords

Color Analysis, Detection, GLCM, Recognition.

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