band signal

The acoustic noise pollution is one of the serious disasters in the current industrialized life. Though traditional solutions based on noise absorption have many different applications، but these methods have low performance for low frequency noises. Active Noise Control (ANC) has been introduced to resolve this problem. In this paper، a new active method is introduced for suppressing acoustic noises based on the reinforcement learning. To achieve this، an algorithm to control periodic noises is suggested. Then، the method is developed further to deal with multi-tonal signals with a large number of harmonics. At the next step، the broad-band signals are considered. The problem is broken into some sub-problems in frequency domain and each is solved via a reinforcement learning approach. In all of the proposed techniques no model for the environment is needed. Combining the reinforcement learning and the traditional methods in ANC for broad-band signals is a new line research considered here. This combination could increase the speed of the response، but some information of the dynamics of the environment is needed. This will cause the system to become compatible with gradual changes of the environment. Simulation results show the effectiveness of the proposed approach.