2025年音频滤波器

科技前沿 • 2025-04-07 20:21 • 阅读 16

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1.高斯滤波器

scipy.ndimage.gaussian_filter1d — SciPy v1.8.0 Manual

对含噪数据进行平滑处理 - MATLAB smoothdata- MathWorks 中国

from scipy.signal import gaussian def gaussian_filter(M, std, sym=True): """ Return a Gaussian window. """ return gaussian(M, std, sym)

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2.均值滤波器

3.中值滤波器

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4.卡尔曼滤波器

讯享网 # based on https://stackoverflow.com/questions//kalman-2d-filter-in-python def kalman_xy(x, P, measurement, R, motion=np.matrix('0. 0. 0. 0.').T, Q=np.matrix(np.eye(4))): """ Args: x: initial state 4-tuple of location and velocity: (x0, x1, x0_dot, x1_dot) P: initial uncertainty convariance matrix measurement: observed position R: measurement noise motion: external motion added to state vector x Q: motion noise (same shape as P) """ return kalman(x, P, measurement, R, motion, Q, F=np.matrix(''' 1. 0. 1. 0.; 0. 1. 0. 1.; 0. 0. 1. 0.; 0. 0. 0. 1. '''), H=np.matrix(''' 1. 0. 0. 0.; 0. 1. 0. 0.''')) def kalman(x, P, measurement, R, motion, Q, F, H): """ Args: x: initial state P: initial uncertainty convariance matrix measurement: observed position (same shape as H*x) R: measurement noise (same shape as H) motion: external motion added to state vector x Q: motion noise (same shape as P) F: next state function: x_prime = F*x H: measurement function: position = H*x Returns: the updated and predicted new values for (x, P) See also http://en.wikipedia.org/wiki/Kalman_filter This version of kalman can be applied to many different situations by appropriately defining F and H. """ # UPDATE x, P based on measurement m # distance between measured and current position-belief y = np.matrix(measurement).T - H * x S = H * P * H.T + R # residual convariance K = P * H.T * S.I # Kalman gain x = x + K * y I = np.matrix(np.eye(F.shape[0])) # identity matrix P = (I - K * H) * P # PREDICT x, P based on motion x = F * x + motion P = F * P * F.T + Q return x, P

5.rasta滤波器

def rasta_filter(x): """ % y = rastafilt(x) % % rows of x = critical bands, cols of x = frame % same for y but after filtering % % default filter is single pole at 0.94 """ numer = np.arange(-2, 3) numer = (-1 * numer) / np.sum(numer * numer) denom = np.array([1, -0.94]) zi = signal.lfilter_zi(numer, 1) y = np.zeros((x.shape)) for i in range(x.shape[0]): y1, zi = signal.lfilter(numer, 1, x[i, 0:4], axis=0, zi=zi * x[i, 0]) y1 = y1 * 0 y2, _ = signal.lfilter(numer, denom, x[i, 4:x.shape[1]], axis=0, zi=zi) y[i, :] = np.append(y1, y2) return y

6.sobel滤波器

讯享网from scipy.ndimage import sobel def sobel_filter(sig, axis=-1, mode="reflect", cval=0.0): """ Calculate a Sobel filter. """ return sobel(sig, axis, mode, cval)

2025年音频滤波器

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