5 results about "Spectral density estimation" patented technology

The invention provides a voice enhancement and high-precision recognition method and system in a complex environment, and relates to the technical field of voice processing, and the method comprises the steps: collecting a time domain signal in an off-road parking sentry box environment for preprocessing, detecting a mute segment signal in a standard time domain signal for noise power spectral density estimation, and obtaining a noise power spectral density value; a reverberation parameter is obtained by combining voice onset information and noise spatial correlation estimation, prediction is performed by using a deep neural network model, voice masking is applied to microphone array signals to perform enhancement processing, adaptive feature extraction is performed on time domain enhanced voice signals, and a voice signal is obtained. And performing high-precision recognition on the voice adaptive feature sequence based on an acoustic model and a language model, and outputting a target recognition text. The technical problems of poor voice signal quality and low recognition accuracy in a complex noise environment in the prior art are solved. The technical effects of improving the voice signal quality and the recognition accuracy and realizing clear, accurate and real-time voice interaction are achieved.

The invention discloses a pulling and lifting cable frequency detection and cable force measurement method and system based on a millimeter wave radar, and belongs to the field of pulling and lifting cable force measurement. The method comprises the steps that vibration signals are collected through the millimeter wave radar to obtain complex data streams, time-frequency analysis is conducted on the complex data streams, and an interference model is established; the method comprises the following steps: reconstructing a noise-reduced vibration signal by adopting adaptive filtering, extracting candidate vibration frequencies through power spectral density estimation and spectral peak detection, calculating corresponding stability scores, and obtaining a first verification factor used for evaluating wind disturbance and a second verification factor used for evaluating vibration frequency deviation; according to the method, through multi-source sensing data fusion and self-adaptive signal processing, complex environment interference is effectively overcome, non-contact high-precision cable force measurement is achieved, and the method has the outstanding advantages of being high in anti-interference capacity, high in measurement reliability and the like.

The invention discloses a multi-domain fusion super-multilevel attention evaluation method and system based on electroencephalogram data, and the method comprises the following steps: collecting an attention electroencephalogram signal of a test person through an electroencephalogram signal collection device, carrying out the filtering processing, and obtaining a processed electroencephalogram signal; carrying out attention state division based on the processed electroencephalogram signal to obtain a state division result; performing time-frequency decomposition on the processed electroencephalogram signal by using fast Fourier transform to obtain a weighted time-domain signal; performing power spectral density estimation on the processed electroencephalogram signals by using a Welch method to obtain a multi-channel fusion image; constructing a parallel model structure, and training the parallel model structure based on the weighted time domain signal, the multi-channel fusion image and the state division result to obtain an attention evaluation model; and collecting attention electroencephalogram signals of the to-be-evaluated person, and evaluating the attention state of the to-be-evaluated person based on the attention evaluation model to obtain an attention evaluation result.

The invention provides a nut flavor intelligent perception evaluation method, device and equipment and a storage medium, frequency domain feature extraction is carried out on electroencephalogram data through a Welch method, power spectral density estimation is carried out according to the extracted frequency domain feature, and the frequency domain feature power spectral density is obtained; for a plurality of preset brain wave segments of different brain regions, calculating a power spectral density average value of the plurality of preset brain wave segments in each brain region according to the frequency domain characteristic power spectral density; and constructing a nut flavor sensing electroencephalogram feature contour based on the average value of the power spectrum densities of the different brain regions. Compared with the prior art that a modern instrument can only measure the content of volatile components, the method constructs a perceived electroencephalogram characteristic contour according to the average value of the power spectrum density of different brain regions, realizes intelligent perception evaluation based on electroencephalogram physiological signals, and provides an objective and quantitative analysis tool for food sensory science.

This invention relates to brain-computer interfaces, specifically to a brain-computer interface-based emotion and stress management system. The control unit acquires the user's multimodal signals using a non-invasive multimodal signal acquisition device and preprocesses the EEG signals using an EEG signal processing module. The control unit then estimates the power spectral density of the preprocessed EEG signals using a power spectral density estimation module to obtain the power spectral density of the EEG signals. A relaxation level acquisition module obtains the user's relaxation level based on the power spectral density of the EEG signals, a coefficient of variation acquisition module obtains the coefficient of variation for the corresponding frequency band based on the power spectral density of the EEG signals, and a Schumann resonance energy index acquisition module obtains the Schumann resonance energy index corresponding to the first and second harmonics based on the power spectral density of the EEG signals. The technical solution provided by this invention overcomes the shortcomings of existing technologies, such as the difficulty in accurately identifying the user's emotional state and the inability to provide personalized real-time intervention.