Speech emotion recognition (SER) system is one of the most important building block in this age of technology, where, the human-computer interaction plays a very indispensable role. In this work, emotional speech samples are taken from two databases namely, Berlin emotional speech database (Emo-DB) and surrey audio-visual expressed emotion speech database (SAVEE). Three different cepstral features like mel-frequency cepstral coefficients (MFCC), human factor cepstral coefficients (HFCC) and gammatone frequency cepstral coefficients (GFCC) are extracted from the emotional speech samples. These features are used for training, validating and testing the classifier. The extracted features represent the emotional content present in the speech signal. Two classifiers namely, the feedforward backpropagation artificial neural network (FF-BP-ANN) and support vector machine (SVM) are used for developing SERs. These classifiers are trained to classify the input speech signals into any one emotion among the distinct emotional classes corresponding to anger, boredom, disgust, fear, happiness, neutral, sadness and surprise. The results corresponding to the usage of three different cepstral features in accurately recognizing the emotions from speech utterances of two databases are presented. Finally, the performance comparisons of SER systems are made with respect to features, classifiers and from existing literature.

In this paper, a robust and highly imperceptible audio watermarking technique is presented based on discrete cosine transform (DCT) and singular value decomposition (SVD). The low-frequency components of the audio signal have been selectively embedded with watermark image data making the watermarked audio highly imperceptible and robust. The imperceptibility of proposed methods is evaluated by computing signal-to-noise ratio and by conducting subjective listening tests. The robustness of proposed technique is evaluated by computing bit error rate and average information loss in retrieved watermark image subjected to MP3 compression, AWGN, re-sampling, re-quantization, amplitude scaling, low-pass filtering, and high-pass filtering attacks with high data payload of 6 kbps. The information-theoretic approach is used to model the proposed watermarking technique as discrete memoryless channel. The Shannon’s entropy concept is used to highlight the robustness of proposed technique by computing the information loss in retrieved watermarked image.

In this paper, Vedic Multiplication Technique is used to implement IEEE 754 Floating point multiplier. The Urdhva-triyakbhyam sutra is used for the multiplication of Mantissa. The underflow and over flow cases are handled. The inputs to the multiplier are provided in IEEE 754, 32 bit format. The multiplier is implemented in VHDL and Virtex-5 FPGA is used.