Please select the most similar patent number from A, B, C and D. Which number is?
A semi-non-negative matrix factorization-based sound signal separation method, comprising: calculating a Fourier transform result of a single-channel mixed sound signal and calculating the frequency spectrum thereof (S1); performing semi-non-negative matrix factorization on the frequency spectrum of the mixed signal (S2); obtaining respective initial frequency spectra of source signals according to the frequency spectrum of the mixed signal (S3); performing semi-non-negative matrix factorization on the initial frequency spectra of the source signals (S4); obtaining frequency spectra of the source signals according to the frequency spectrum of the mixed signal and a corresponding feature matrix and coefficient matrix thereof, and feature matrices and coefficient matrices corresponding to the source signals (S5); obtaining Fourier transform results of the source signals according to the Fourier transform result of the mixed signal and the frequency spectra of the source signals (S6); and performing inverse Fourier transform on the Fourier transform results of the source signals to obtain the source signals, thereby achieving the separation of the source signals from the single-channel mixed sound signal (S7). The described method may be used to separate a single-channel mixed sound signal formed by mixing source sound signals that overlap in a frequency domain and that are not necessarily independent of each other.
A: A radar signal processing method, system and apparatus based on a photonic fractional Fourier transformer. The radar signal processing method comprises: transmitting a linear frequency modulation signal to a target to be detected, receiving an echo signal of said target, and loading the linear frequency modulation signal and the echo signal onto a single-frequency optical wave by means of an electro-optic modulator (S1); respectively biasing a sub-modulator and a parent modulator of the electro-optic modulator at different bias points, modulating the single-frequency optical wave by means of the electro-optic modulator, and outputting a modulated optical signal (S2); converting the optical signal by means of a photoelectric detector to obtain a photocurrent (S3); and performing a Fourier transform on the photocurrent to obtain a fractional Fourier spectrum, and obtaining distance information of said target according to a peak position of each pulse signal in the fractional Fourier spectrum (S4). A high-frequency broadband radar signal is converted into a low-frequency narrowband fractional-domain signal, and the interference of a false target component on a radar detection result is thus eliminated.
B: Provided are a speech separation method and a device, a medium and an electronic device. The method comprises: S300, acquiring a time-frequency domain mixed signal to be processed; S301, obtaining a separation matrix of the time-frequency domain mixed signal according to the time-frequency domain mixed signal and a cost function of independent vector analysis of a Gaussian mixture model comprising an amplitude adjustment factor; and S302, obtaining a time-frequency domain signal of each sound source from the time-frequency domain mixed signal according to the separation matrix. The method is beneficial to improving the implementability of speech separation technology, thereby broadening the scope of application of the speech separation technology.
C: A method for classification of mixed signals, comprising: receiving mixed signals; calculating a matrix corresponding to the mixed signals by means of a preset principal component analysis method, obtaining mixed signals to be classified, and determining the number of signal types comprised in the mixed signals to be classified; determining a separation matrix according to the number of signal types comprised in the mixed signals to be classified; separating, by means of the separation matrix, various signals in the mixed signals to be classified to obtain signals to be identified; calculating a preset number of high-order cumulants corresponding to each signal to be identified in the signals to be identified; using the calculated high-order cumulants as characteristics of the signal to be identified corresponding to the high-order cumulants; inputting the characteristics of the signal to be identified to a preset classification model; and obtaining an modulation mode of the signal to be identified. The method provided in the embodiments of the present application has no requirements for the classification environment, while in the prior art, specific conditions need to be satisfied to classify mixed signals. Therefore, the present application has universal applicability.
D: A time-frequency masking and deep neural network-based sound source orientation estimation method and device, an electronic device and a storage medium, belonging to the field of computer technology. Said method comprises: acquiring sound signals of multiple channels (S110); performing frame division, windowing and Fourier transformation on a sound signal of each channel in the signals of multiple channels, so as to form a short-time Fourier spectrum of the sound signals of multiple channels (S120); performing an iterative operation on the short-time Fourier spectrum by means of a pre-trained neural network model, so as to calculate ratio filters corresponding to target signals in the sound signals of multiple channels (S130); fusing the plurality of ratio filters to form a single ratio filter (S140); and performing masking and weighting on the signals of multiple channels by means of the single ratio filter, so as to determine the orientation of a target sound source (S150). The time-frequency masking and deep neural network-based sound source direction estimation method and device can have strong robustness in an environment of a low signal-to-noise ratio and strong reverberation, and improve the accuracy and stability of target sound source direction estimation.
Answer: B