Multi Utility E-Controlled cum Voice Operated Farm Vehicle
ABSTRACT This paper describes the design and construction of MUEVOFV.The vehicle will be used to explore ways of increasing theproductivity using expensive agricultural mobile machinery bytaking over some of the tasks of the operator, allowing him tocontrol the machinery from remote place i.e. E-control throughvoice commands; and to control several accessories of themachine simultaneously. The system was designed to satisfy theneeds of various farm operations in unknown agricultural fields.The controller has a layered architecture and supports two degreesof cooperation using sensor modules between the operator androbotic vehicle, direct and supervisory control. The vehicle'sposition and heading direction can be controlled by globalpositioning.
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ABSTRACT: Nowadays classification of gender is one of the most important processes in speech processing. Usually gender classification is based on considering pitch as feature. The pitch value of female is higher than the male. In most of the recent research works gender classification process is performed using the abovementioned condition. In some cases the pitch value of male is higher and also pitch of some female is low, in that case this classification does not produce the exact required result. By considering the aforementioned problem we have here proposed a new method for gender classification method which considers three features. The new method uses fuzzy logic and neural network to identify the gender of the speaker. To train fuzzy logic and neural network, training dataset is generated by using the above three features. Then mean value is calculated for the obtained result from fuzzy logic and neural network. By using this threshold value, the proposed method identifies the speaker belongs to which gender. The implementation result shows the performance of the proposed technique in gender classification.
Conference Paper: Automatic male-female voice discrimination[Show abstract] [Hide abstract]
ABSTRACT: In this work, we have presented a novel simple scheme for classifying audio speech signals into male speech and female speech. In the context of content-based multimedia indexing gender identification based on speech signal is an important task. Some popular salient low level time-domain acoustic features which are very closely related to the physical properties of source audio signal like zero crossing rate (ZCR), short time energy (STE) along with spectral flux, a low level frequency domain feature, are used for this discrimination. RANSAC and Neural-Net has been used as classifier. The experimental result exhibits the efficiency of the proposed scheme.2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT); 02/2014
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ABSTRACT: In speech processing, gender clustering and classification plays a major role. In both gender clustering and classification, selecting the feature is an important process and the often utilized feature for gender clustering and classification in speech processing is pitch. The pitch value of a male speech differs much from that of a female speech. Normally, there is a considerable frequency value difference between the male and female speech. But, in some cases the frequency of male is almost equal to female or frequency of female is equal to male. In such situation, it is difficult to identify the exact gender. By considering this drawback, here three features namely; energy entropy, zero crossing rate and short time energy are used for identifying the gender. Gender clustering and classification of speech signal are estimated using the aforementioned three features. Here, the gender clustering is computed using Euclidean distance, Mahalanobis distance, Manhattan distance & Bhattacharyya distance method and the gender classification method is computed using combined fuzzy logic and neural network, neuro fuzzy and support vector machine and its performance are analyzed.