dft.py 文件源码

def dft_np(signal, hop_size=256, fft_size=512):
    n_hops = len(signal) // hop_size
    s = []
    hann_win = hann(fft_size)
    for hop_i in range(n_hops):
        frame = signal[(hop_i * hop_size):(hop_i * hop_size + fft_size)]
        frame = np.pad(frame, (0, fft_size - len(frame)), 'constant')
        frame *= hann_win
        s.append(frame)
    s = np.array(s)
    N = s.shape[-1]
    k = np.reshape(np.linspace(0.0, 2 * np.pi / N * (N // 2), N // 2), [1, N // 2])
    x = np.reshape(np.linspace(0.0, N - 1, N), [N, 1])
    freqs = np.dot(x, k)
    reals = np.dot(s, np.cos(freqs)) * (2.0 / N)
    imags = np.dot(s, np.sin(freqs)) * (2.0 / N)
    return reals, imags