招商网站的建设意义多商城源码

一、概述

        本节对delay sub算法进行仿真。更多资料和代码可以进入https://t.zsxq.com/qgmoN ，同时欢迎大家提出宝贵的建议，以共同探讨学习。

二、代码仿真

import numpy as np
import soundfile as sf
import scipy
import matplotlib.pyplot as pltfft_size = 256
freq_bin = 129def calculate_circular_array_steering_vector(angle, r=0.0463, N=6, fs=16000, fft_size=256, c=343):steering_vector = np.zeros((N, fft_size//2 + 1), dtype=complex)for f in range(int(fft_size/2+1)):for n in range(N):frequency = fs * f / fft_sizeif frequency == 0:phase_delay = 0steering_vector[n, f] = np.exp(1j * phase_delay)else:lambda_val = c / frequencytheta_mic = -2 * np.pi * n / N + 2 * np.pitheta_signal = np.pi * angle / 180phase_delay = 2 * np.pi * np.cos(theta_signal - theta_mic) * r / lambda_valsteering_vector[n, f] = np.exp(1j*phase_delay)return steering_vectordef calculate_circular_array_steering_vector_anticlockwise(angle, r=0.0463, N=6, fs=16000, fft_size=256, c=343):steering_vector = np.zeros((N, fft_size // 2 + 1), dtype=complex)for f in range(int(fft_size / 2 + 1)):for n in range(N):frequency = fs * f / fft_sizeif frequency == 0:phase_delay = 0steering_vector[n, f] = np.exp(1j * phase_delay)else:lambda_val = c / frequencytheta_mic = 2 * np.pi * n / Ntheta_signal = np.pi * angle / 180phase_delay = 2 * np.pi * np.cos(theta_signal - theta_mic) * r / lambda_valsteering_vector[n, f] = np.exp(1j * phase_delay)return steering_vectordef delay_sub(a, data):data1 = np.multiply(np.conjugate(a), data)data2 = np.sum(data1, axis=0) / 6result = np.zeros((freq_bin,), dtype=complex)for i in range(freq_bin):data_i = data1[:, i]data_ds = data2[i]for ch in range(5):result[i] += data_i[ch+1] - data_i[ch]result[i] /= 5return resultdef main():# 读取WAV文件data, samplerate = sf.read('output/simulate_role1_0_t60_0.2_role2_180_t60_0.2.wav')# 定义帧长和帧移frame_length = int(samplerate * 0.016)  # 25ms帧长frame_step = int(samplerate * 0.008)  # 10ms帧移# 创建汉明窗hamming_window = scipy.signal.windows.hamming(frame_length)hamming_window = np.reshape(hamming_window, [frame_length, 1])sample_num = data.shape[0] - frame_length + 1HH = calculate_circular_array_steering_vector(180)# 手动分帧和加窗frames = []out1 = np.zeros(int(fft_size/2), dtype=float)for i in range(0, sample_num, frame_step):frame = data[i:i + frame_length, :]windowed_frame = frame * hamming_windowfft_frame = np.fft.fft(windowed_frame, axis=0)fft_frame1 = np.transpose(fft_frame[:freq_bin, :])fft_frame1 = delay_sub(HH, fft_frame1)#1micfft_frame11 = fft_frame1fft_frame21 = np.concatenate((fft_frame11, fft_frame11[1:-1][::-1].conj()))fft_frame21 = np.transpose(fft_frame21)ifft_frame1 = np.fft.ifft(fft_frame21)short_data1 = ifft_frame1[:int(fft_size/2)] + out1out1 = ifft_frame1[int(fft_size/2):]frames.extend(short_data1)frames1 = np.array(frames).reshape((-1)).realsf.write("output/simulate_role1_0_t60_0.2_role2_180_t60_0.2_out_delaysub_t0.wav", frames1, 16000)main()

三、结果展示

3.1 0度为干扰方向

3.2 180度为干扰方向

四、总结

        从结果上看，使用delay sub明显比使用delay sum对噪声的抑制效果要好。