Monaural source separation is an interesting area that has received much attention in the signal processing community as it is a pre-processing step in many applications. However, many solutions have been developed to achieve clean separation based on Non-Negative Matrix Factorization (NMF). In this work, we proposed a variant of Itakura-Saito Divergence NMF based on source filter model that captures the temporal continuity of speech signal. The algorithm shows a very good separation results for mixture of two speech sources in terms of artifacts reduction. Besides that, Source to distortion ratio (SDR) and Source to Artifact Ratio (SAR) were found to be higher when compared with NMF algorithms with Kullback-Leibler and Euclidean divergences.
Storage of water is common to all human for domestic and industrial utilization. Today, water tanks are the conventional and a major component in water storage systems as they have the capacity to hold large volume of water over a long period of time. They come in different shapes and sizes. However inadequate monitoring of water level in this storage facility may lead to either shortage of water supply when needed or its wastage during lifting process. Hence, there is need for proper monitoring of water level in a storage facility. This need motivates this work, which is the development of water level monitoring system that based on sonar technology. Materials employed in the development include ultrasonic sensor module, ATmega-328P microcontroller, 16MHz crystal oscillator, 12V dc water pump among others. The developed system monitors the water level and activates the pump as appropriate based on the input the ultrasonic sensor gives to the microcontroller which occupies the centre of the system. The developed system has potential of mitigating the attendant problems that usually arise from wastage or shortage of water supply with respect to water storage facility. Keywords—SONAR, water storage, monitoring system
Interaction of acoustic signals when several audio sources are active simultaneously results in the disturbance of estimation of an individual source by co-occurring sounds. Data decomposition therefore constitutes one of the core tasks in monaural source separation. Particularly, in semi-supervised learning approach, viable means of achieving this is through the application of Non-negative Matrix Factorization (NMF). Owing to a paucity of information on the application of this method, especially in a speech system, evaluation of some cost functions in NMF-based monaural speech decomposition was investigated in this study. A generalized gradient descent algorithm is derived for the minimization while three cost functions: Euclidean Distance, Kullback-Leibler Divergence and Itakura-Saito divergences are applied to the derived separation NMF algorithm. These divergences are evaluated using experimental data while the performance of each of these is evaluated based on the cost values and convergence rate. Itakura-Saito divergence yields optimal performance over the other two divergences for given number of iterations and number of channels. Keywords— Cost functions, non-negative matrix factorization, speech separation, evaluation
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