Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (2)
  • Open Access

    ARTICLE

    Real-Time Speech Enhancement Based on Convolutional Recurrent Neural Network

    S. Girirajan, A. Pandian*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1987-2001, 2023, DOI:10.32604/iasc.2023.028090 - 19 July 2022

    Abstract Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output. In recent years, the need for speech enhancement has been increased due to challenges that occurred in various applications such as hearing aids, Automatic Speech Recognition (ASR), and mobile speech communication systems. Most of the Speech Enhancement research work has been carried out for English, Chinese, and other European languages. Only a few research works involve speech enhancement in Indian regional Languages. In this paper, we propose a two-fold architecture to perform speech enhancement for Tamil speech signal… More >

  • Open Access

    ARTICLE

    Speckle Noise Suppression in Ultrasound Images Using Modular Neural Networks

    G. Karthiha*, Dr. S. Allwin

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1753-1765, 2023, DOI:10.32604/iasc.2023.022631 - 19 July 2022

    Abstract In spite of the advancement in computerized imaging, many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis. In this way, the research in the zone of image denoising is very dynamic. Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise suppression is a basic and productive methodology. In this paper, Bilateral Filter (BF) based Modular Neural Networks (MNN) has been utilized for speckle noise suppression in the ultrasound image. Initial step the BF filter is used to filter the input image.… More >

Displaying 1-10 on page 1 of 2. Per Page