显然，当写入未规范化的64位浮点数据时会出现问题。通过将x转换为16位或32位整数，或者将x规范化为范围[-1，1]并转换为32位浮点，我得到了一个听起来合理的输出文件。
我没有使用sounddevice;相反，我将过滤后的数据保存到一个新的WAV文件中并播放它。以下是对我有效的几种变体：

# Convert to 16 integers

wavfile.write('off_plus_noise_filtered.wav', sr, x.astype(np.int16))

或者...

# Convert to 32 bit integers

wavfile.write('off_plus_noise_filtered.wav', sr, x.astype(np.int32))

或者...

# Convert to normalized 32 bit floating point

normalized_x = x / np.abs(x).max()
wavfile.write('off_plus_noise_filtered.wav', sr, normalized_x.astype(np.float32))

输出整数时，您可以放大值，以将截断浮点值所造成的精确度损失降至最低：

x16 = (normalized_x * (2**15-1)).astype(np.int16)
wavfile.write('off_plus_noise_filtered.wav', sr, x16)

以下代码用于从此处生成带通滤波器：https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass

from scipy.signal import butter, lfilter

def butter_bandpass(lowcut, highcut, fs, order=5):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')
    return b, a

def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = lfilter(b, a, data)
    return y

if __name__ == "__main__":
    import numpy as np
    import matplotlib.pyplot as plt
    from scipy.signal import freqz

    # Sample rate and desired cutoff frequencies (in Hz).
    fs = 5000.0
    lowcut = 500.0
    highcut = 1250.0

    # Plot the frequency response for a few different orders.
    plt.figure(1)
    plt.clf()
    for order in [3, 6, 9]:
        b, a = butter_bandpass(lowcut, highcut, fs, order=order)
        w, h = freqz(b, a, worN=2000)
        plt.plot((fs * 0.5 / np.pi) * w, abs(h), label="order = %d" % order)

    plt.plot([0, 0.5 * fs], [np.sqrt(0.5), np.sqrt(0.5)],
             '--', label='sqrt(0.5)')
    plt.xlabel('Frequency (Hz)')
    plt.ylabel('Gain')
    plt.grid(True)
    plt.legend(loc='best')

    # Filter a noisy signal.
    T = 0.05
    nsamples = T * fs
    t = np.linspace(0, T, nsamples, endpoint=False)
    a = 0.02
    f0 = 600.0
    x = 0.1 * np.sin(2 * np.pi * 1.2 * np.sqrt(t))
    x += 0.01 * np.cos(2 * np.pi * 312 * t + 0.1)
    x += a * np.cos(2 * np.pi * f0 * t + .11)
    x += 0.03 * np.cos(2 * np.pi * 2000 * t)
    plt.figure(2)
    plt.clf()
    plt.plot(t, x, label='Noisy signal')

    y = butter_bandpass_filter(x, lowcut, highcut, fs, order=6)
    plt.plot(t, y, label='Filtered signal (%g Hz)' % f0)
    plt.xlabel('time (seconds)')
    plt.hlines([-a, a], 0, T, linestyles='--')
    plt.grid(True)
    plt.axis('tight')
    plt.legend(loc='upper left')

    plt.show()

看看这对你有没有帮助。
您可以在此处指定所需的频率：

# Sample rate and desired cutoff frequencies (in Hz).

        fs = 5000.0
        lowcut = 500.0
        highcut = 1250.0