Self-learning automatic noise reduction system and method of kitchen ventilator

Abstract

The invention discloses a self-learning automatic noise reduction system and method of a kitchen ventilator. The system mainly comprises a flue gas turbine, a microphone and a loudspeaker are arranged on the flue gas turbine, after the flue gas turbine is assembled, and before the turbine leaves the factory, the turbine is subject to primary pre-alignment, and the pre-alignment data serve as following field actual running preset benchmark data, and after the flue gas turbine is mounted at the user home, the flue gas turbine runs at the different rotating speeds; flue gas turbine noise and environment noise at different rotating speed segments are collected through the microphone, collected measured data are fed back to a signal processing module on the glue gas turbine, and a self-learning noise reduction algorithm module is combined with the measured data of the flue gas turbine noise and the environment noise to generate a corresponding sound waveform according to the benchmark data preset in a pre-alignment module; the sound waveform is output through the loudspeaker and is used for offsetting most part of noise. The system has the beneficial effects that the main noise component can be effectively removed, a certain obvious effect is achieved, a corresponding sound field and a complex algorithm do not need to be built, and meanwhile, expensive hardware circuit supporting does not need to be built.

Description

technical field [0001] The invention relates to the field of noise reduction for range hoods, and mainly relates to a self-learning automatic noise reduction system and method for range hoods. Background technique [0002] In the field of hoods, after each hood is assembled, it will basically correspond to a fixed noise curve under different operating conditions in a certain environment, such as 200RPM corresponds to curve A, 900RPM corresponds to curve B, etc. The traditional active noise reduction algorithm generally establishes a near-sound field model based on the actual environment and working conditions of the hood, and then inputs the parameters of the model into the algorithm for processing, and then drives the speaker to generate a sound waveform opposite to the noise to offset the noise based on the output result. Because the actual installation environment is different, the actual operating conditions are also different, resulting in very complex models and algori...

AI Technical Summary

Problems solved by technology

The traditional active noise reduction algorithm generally establishes a near-sound field model based on the actual environment and working conditions of the hood, and then inputs the parameters of the model into the algorithm for processing, and then drives the speaker to generate a sound waveform opposite to the noise to offset the noise based on the output result. Because the actual installation environment is different, the working conditions during actual operation are also different, resulting in very complicated models and algorithms and high cost. The final actual noise reduction effect is quite different from the theoretical one, and the effect is not very good.

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