7,698 research outputs found

    Automatic Raaga Identification System For Carnatic Music Using Hidden Markov Model

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    As for as the Human Computer Interactions (HCI) is concerned, there is broad range of applications in the area of research in respective of Automatic Melakarta Raaga Identification in music. The pattern of identification is the main object for which, the basic mathematical tool is utilized. On verification, it is observed that no model is proved consistently and effectively to be predicted in its classification. This paper is, therefore, introduces a procedure for Raaga Identification with the help of Hidden Markov Models (HMM) which is rather an appropriate approach in identifying Melakarta Raagas. This proposed approach is based on the standard speech recognition technology by using Hidden continuous Markov Model. Data is collected from the existing data base for training and testing of the method with due design process relating to Melakarta Raagas. Similarly, to solve the problem of automatic identification of raagas, a suitable approach from the existing database is presented. The system, particularly, this model is based on a Hidden Markov Model enhanced with Pakad string matching algorithm. The entire system is built on top of an automatic note transcriptor. At the end, detailed elucidations of the experiments are given. It clearly indicates the effectiveness and applicability of this method with its intrinsic value and significance

    Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

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    Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks
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