Research On Active Noise Control In Open Space For Emotion Regulation

With economic development,the demand for quietness to improve the working and living environment is becoming more and more intense.Because of the double harmfulness of environmental noise to human physiology and psychology,weakening and even eliminating environmental noise has become an important issue related to human physical and mental health in recent years.Long-term exposure to noise will not only bring great health risks to humans,but also produce serious negative emotions,such as irritability and anger.Human beings are influenced by emotion all the time.Emotion is a vital factor in human daily life.Active noise control is the mainstream environmental noise reduction technology at present,but there are some problems,such as uncertain noise reduction range,narrow noise reduction space,lack of objective evaluation of emotion regulation methods in noise reduction environment.Environmental noise control of open space environment,and establish a noise reduction environment.Take the emotion regulation under the noise reduction environment as the typical application scenario under the noise control of open space.Further research on noise reduction environment of emotion regulation method to expand the people-oriented connotation of the social health care system and quality improvement has important implications.According to the above requirements,this dissertation takes active noise control as a technical means to study the directional long-distance noise control,open space noise control,emotion regulation data processing in noise reduction environment and emotion regulation methods.The main contents of the full text are as follows:(1)In order to realize directional remote noise control,an adjustable parametric array loudspeaker is used as a secondary source to emit the cancel acoustic waves based on online secondary path modeling method.Based on the parametric array theory and the high directionality of the parametric array loudspeaker,a secondary path modeling method using adjustable parameter array loudspeaker is proposed,which is used to eliminate different long-distance noise in real time.Aiming at the open space,the power and size of parametric array loudspeaker are adjusted adaptively according to the different distance of the target to control the noise in different remote target areas.In order to track the noise reduction targets at different distances in real time,an online secondary path modeling method with variable auxiliary noise power is adopted.The adjustable parametric array loudspeaker as the secondary source has the advantages of longer propagation distance,smaller acoustic feedback and more regular and controllable distribution of the quiet area.(2)In order to realize noise control in open space,a noise control method based on loudspeaker omni-directional array is proposed.Aiming at open space,the omni-directional array model of spherical loudspeaker is established based on the spatial coverage of loudspeaker acoustic wave propagation.In order to simplify the three-dimensional space model,a spatial longitude and latitude mapping model is proposed to express and manage the target trajectory tracking visually.Based on the omni-directional array of loudspeaker,the real-time sensing and dynamic tracking of target position are realized.According to the target moving position,the corresponding loudspeaker is adaptively selected as the center node.The quiet area is constructed in the target area by the normalized filtering-x least mean square error algorithm,and the average noise reduction for narrowband noise is about 14 d B,so that the system has the ability of full space coverage.Through the spherical loudspeaker omni-directional array,the dynamic tracking and visual management of the target can be effectively realized,and the quiet area can be effectively constructed in the target area.(3)In order to study the data processing model of emotion regulation under noise reduction,a brain wave signal processing model based on musical staff is proposed.In the open space noise control environment,a multi-channel EEG sensor network is constructed to measure the EEG signals of the tested individuals under different emotional states,and the EEG-emotional state mapping library is established.The EEG time domain signal is segmented adaptively by automatic multi-scale extreme point detection algorithm,and the EEG signal is converted by musical staff model.The time-frequency characteristics of brainwaves,such as amplitude,contour and signal frequency,are expressed quantitatively in a standardized music space to obtain the staff index.The data can be serialized,compressed and stored while preserving the time-frequency characteristics of brainwave.The staff index is used as the joint feature quantity and the stacked sparse auto-encoders neural network classification algorithm is used to recognize the emotional state.Compared with the traditional feature quantity,the classification accuracy is improved by 10.35%.Without additional resources,brainwave not only has ―sound‖ effect,but also has advantages in data mining and serialization storage,signal reconstruction,recognition rate.The staff music corresponding to brainwave is generated through this model,which can be used as media to provide reference for people to understand the change of emotional state.(4)In order to carry out the emotion regulation under the noise reduction environment,reconfigurable music segments are used to adjust the emotion state adaptively.Noise has a negative impact on emotion.Music is used to regulate emotion to improve emotional state.Therefore,in the open space noise control environment,a multi-channel EEG sensor network is constructed to collect the brain waves of the participants,and then segment them to establish a personalized reconfigurable music-EEG library corresponding to the participant.Power spectrum distribution,frequency value corresponding to peak point of power spectrum,approximate entropy,Hurst index and wavelet entropy are extracted from EEG signals as joint features.Arousal degree and valence emotion dimension model is adopted,and stacked sparse auto-encoders neural network is used to classify emotional states and establish emotional evaluation indicators.Music segments are selected from the library of reconfigurable music segments for reconstruction,combination and playback,and emotion regulation under noise reduction environment.Compared with complete music,the number of irrelevant emotional states is reduced by 69.92%,and the recognition rate of emotional states is improved by 31.32%.Compared with 2D-SAM scale for subjective emotional evaluation,the established emotional evaluation index is more objective and novel,and greatly reduces the difficulty of data processing.