啸叫抑制（AFS）从算法仿真到工程源码实现-第四节-数据仿真

一、概述

        本节我们使用python代码模拟啸叫的产生。

二、数据仿真

2.1 仿真流程图

2.2 仿真代码

#!/usr/bin/python
from __future__ import division
# import

import numpy as np
import matplotlib.pyplot as plt
import soundfile as sf


def simulate_howling_env():
    input_file = "test/LDC93S6A.wav"
    howling_file = "test/added_howling.wav"

    # load clean speech file
    x, Srate = sf.read(input_file)

    # pre design a room impulse response
    rir = np.loadtxt('test/path.txt', delimiter='\t')

    # G : gain from mic to speaker
    G = 0.2

    # ====== set parameters ========
    interval = 0.02  # frame interval = 0.02s
    Slen = int(np.floor(interval * Srate))
    if Slen % 2 == 1:
        Slen = Slen + 1
    PERC = 50  # window overlap in percent of frame size
    len1 = int(np.floor(Slen * PERC / 100))
    len2 = int(Slen - len1)
    nFFT = 2 * Slen

    N = min(2000, len(rir))  # limit room impulse response length
    x2 = np.zeros(N)  # buffer N samples of speaker output to generate acoustic feedback
    y = np.zeros(len(x))  # save speaker output to y
    y1 = 0.0  # init as 0
    for i in range(len(x)):
        x1 = x[i] + y1
        y[i] = G * x1
        y[i] = min(2, y[i])  # amplitude clipping
        y[i] = max(-2, y[i])
        x2[1:] = x2[:N - 1]
        x2[0] = y[i]
        y1 = np.dot(x2, rir[:N])

    sf.write(howling_file, y, Srate)
    plt.figure()
    plt.subplot(2, 1, 1)
    plt.plot(y)
    plt.xlim((0, len(y)))
    plt.subplot(2, 1, 2)
    plt.specgram(y, NFFT=nFFT, Fs=Srate, noverlap=len2, cmap='jet')
    plt.ylim((0, 5000))
    plt.ylabel("Frquency (Hz)")
    plt.xlabel("Time (s)")
    plt.show()


simulate_howling_env()

2.3 原始数据

2.4 生成的啸叫数据

三、总结

        本节我们使用代码对啸叫的产生进行了仿真，以方便对算法进行验证。