我们目前正在集成三个代码1.麦克风录音机2.积分器(低通滤波) 3.应用滤波器
我们遇到了这个错误:
fltrd()
[[ 0 0]
[ -1 0]
[ 0 0]
...,
[-65 -60]
[-31 -52]
[-45 -53]]
Traceback (most recent call last):
File "<ipython-input-2-72cbac6fd2ac>", line 1, in <module>
fltrd()
File "C:/Users/vsecadesang/Desktop/5th year/2nd sem/SIGNLAB/PROJECT/etc/project.py", line 57, in fltrd
a2 = integ(x)
File "C:/Users/vsecadesang/Desktop/5th year/2nd sem/SIGNLAB/PROJECT/etc/project.py", line 49, in integ
y[0] = x[0]
ValueError: setting an array element with a sequence.麦克风录音部分工作正常。唯一的问题是,当我们将麦克风录音的wav文件放入积分器中并调用定义的过滤器时,它会生成上面提到的ValueError。我们计划的输出是,我们要放入积分器(低通)滤波器的麦克风录音将产生相同的录音,但噪声更少。有点像有源降噪器。我们不知道出了什么问题,请帮帮忙。
下面是我们的代码:
import pyaudio
import wave
import matplotlib.pyplot as plt
import numpy as np
import scipy.io.wavfile
import scipy.signal as sp
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
CHUNK = 1024
RECORD_SECONDS = 5
WAVE_OUTPUT_FILENAME = "file1.wav"
audio = pyaudio.PyAudio()
# start Recording
stream = audio.open(format=FORMAT, channels=CHANNELS,
rate=RATE, input=True,
frames_per_buffer=CHUNK)
print ("recording...")
frames = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
frames.append(data)
print ("finished recording")
# stop Recording
stream.stop_stream()
stream.close()
audio.terminate()
waveFile = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
waveFile.setnchannels(CHANNELS)
waveFile.setsampwidth(audio.get_sample_size(FORMAT))
waveFile.setframerate(RATE)
waveFile.writeframes(b''.join(frames))
waveFile.close()
x = scipy.io.wavfile.read('file1.wav')
n = x[1]
def integ(x):
y = np.zeros(len(x))
for i in range(0, len(x)):
if i == 0:
y[0] = x[0]
else:
w = y[i-1]
y[i] = w + x[i]
return y
def fltrd():
n,x = scipy.io.wavfile.read('file1.wav')
print(x)
a2 = integ(x)
a3 = np.asarray(a2, dtype = np.int16)
scipy.io.wavfile.write('file2.wav',n,a3)发布于 2018-04-02 19:51:51
您的文件有两个通道,因此x具有某个整数m的形状(m, 2)。在您的函数integ(x)中,您有y = np.zeros(len(x))。这将创建一个具有shape (m,)的数组。然后,行y[0] = x[0]尝试将x[0]中的两个值复制到单个值y[0]中。
要解决此问题,请创建与x形状相同的y
y = np.zeros(x.shape)请注意,默认情况下,np.zeros()会创建一个浮点数组。如果您希望y具有与x相同的数据类型,您可以这样做
y = np.zeros_like(x)您还可以显式设置数据类型,因此,如果您希望y为32位浮点:
y = np.zeros(x.shape, dtype=np.float32)最后,请注意,您的integ()函数是x沿第一个轴的累加和,因此可以将其简化为y = x.cumsum(axis=0)。也就是说,您可以完全删除函数integ,并替换
a2 = integ(x)使用
a2 = x.cumsum(axis=0)这样做的一个潜在问题是,您可能会在16位求和中出现溢出。如果发生这种情况,而你不知道如何处理它,那么,你可以随时在stackoverflow上创建一个关于它的新问题。:)
https://stackoverflow.com/questions/49607395
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