Informatics Publishing Limited

B. Kirubagari , S. Palanivel

Abstract

Digital Speech coding is a procedure to represent a digitized speech signal using as few bits as possible, maintaining the speech quality and its intelligibility at the same time. In this paper a new direction in research on speech coding using auto associative neural networks (AANN) is discussed. The AANN acts as a combination of encoder and decoder. The feature extractor extracts the necessary features from the input speech. Instead of coding the speech signal the Linear Predictive coefficients (LPC) and discrete cosine transform (DCT) features of the speech signal which acts as the compressed value of the speech, is passed to the neural network. The signal reconstructor reconstructs the signal based on the decompressed features and the weight matrix. Different features are extracted and the results are compared. The signal to noise ratio (SNR) shows the efficiency of the algorithm. Some of the applications for which this coder is suitable are videoconferencing, streaming audio, archival, and messaging.

Keywords

Auto Associative Neural Networks, Discrete Cosine Transform, Linear Predictive Coefficients, Speech Coding.

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P. Dhanalakshmi ¹, S. Palanivel ¹, M. Arul ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Tamil Nadu, IN
2 Department of Business Administration, Annamalai University, Tamil Nadu, IN

Abstract

In this paper, we describe automatic segmentation methods for audio broadcast data. Today, digital audio applications are part of our everyday lives. Since there are more and more digital audio databases in place these days, the importance of effective management for audio databases have become prominent. Broadcast audio data is recorded from the Television which comprises of various categories of audio signals. Efficient algorithms for segmenting the audio broadcast data into predefined categories are proposed. Audio features namely Linear prediction coefficients (LPC), Linear prediction cepstral coefficients, and Mel frequency cepstral coefficients (MFCC) are extracted to characterize the audio data. Auto Associative Neural Networks are used to segment the audio data into predefined categories using the extracted features. Experimental results indicate that the proposed algorithms can produce satisfactory results.

Keywords

Linear Prediction Cepstral Coefficients, Mel Frequency Cepstral Coefficients, Auto Associative Neural Networks, Audio Segmentation, Audio Classification.

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J. Sujanaa ¹, S. Palanivel ¹

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN

Abstract

This work proposes an emotion detection approach using Histogram of Oriented Gradients algorithm. Emotion detection is a crucial area since the emotions are extremely person dependent and finding them is hard with various lightning and illumination changes. Most of the works in this field focus on predicting the emotion using the facial region. In the proposed work, emotion detection is done using the mouth region. The dataset is comprised of mouth images containing emotions such as happy, normal and surprised in the form of video frames. The mouth regions are detected using the Haar-Based Cascade classifier at 20 frames per second. The HOG features are then extracted to detect three emotions namely Happy, Normal and Surprised. These HOG features are then trained using One-Dimensional Convolutional Neural Network (1D-CNN). The experimental results show that the proposed system can identify the emotions which gave improved performance than the earlier works.

Keywords

Convolutional Neural Networks, Emotion Recognition, Histogram of Oriented Gradients, Mouth Detection.

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