The parallelisation of big data is emerging as an important framework for large-scale parallel data applications such as seismic data processing. The field of seismic data is so large or complex that traditional data processing software is incapable of dealing with it. For example, the implementation of parallel processing in seismic applications to improve the processing speed is complex in nature. To overcome this issue, a simple technique which that helps provide parallel processing for big data applications such as seismic algorithms is needed. In our framework, we used the Apache Hadoop with its MapReduce function. All experiments were conducted on the RedHat CentOS platform. Finally, we studied the bottlenecks and improved the overall performance of the system for seismic algorithms (stochastic inversion).
In this research work, a novel approach to emotion identification system is proposed for implementation in audio domain using human speech. In order to undertake the new approach, average relative bin frequency coefficients will be extracted from speech. In a noisy environment, audio data are not strictly aligned, thus getting proper noiseless signal is a challenge. Consequently, this affects the performance of emotion detection system. Due to these reasons, a newly proposed approach of Average Relative Bin Frequency technique in frequency domain will be implemented through audio data. Support vector machine with radial basis kernel will be used for the classification. Preliminary results showed an average of 86% accuracy for average relative frequency bin coefficients.Index Terms-Support vector machine, machine learning, relative frequency bin coefficients, information retrieval.
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