Adaptive Noise Cancellation Using Online Self-Enhanced Fuzzy Filters with Applications to Multimedia Processing

Adaptive noise cancellation is a significant research issue in multimedia signal processing, which is a widely used technique in teleconference systems, hands-free mobile communications, acoustical echo and feedback cancellation and so on. For the purpose of implementing real-time applications in nonlinear environments, an online self-enhanced fuzzy filter for solving adaptive noise cancellation is proposed. The proposed online self-enhanced fuzzy filter is based on radial-basis-function networks and functionally is equivalent to the Takagi-Sugeno-Kang fuzzy system. As a prominent feature of the online self-enhanced fuzzy filter, the system is hierarchically constructed and self-enhanced during the training process using a novel online clustering strategy for structure identification. In the process of system construction, instead of selecting the centers and widths of membership functions arbitrarily, an online clustering method is applied to ensure reasonable representation of input terms. It not only ensures proper feature representation, but also optimizes the structure of the filter by reducing the number of fuzzy rules. Moreover, the filter is adaptively tuned to be optimal by the proposed hybrid sequential algorithm for parameters determination. Due to online self-enhanced system construction and hybrid learning algorithm, low computation load and less memory requirements are achieved. This is beneficial for applications in real-time multimedia signal processing.