声压 sound pressure

声压就是大气压受到声波扰动后产生的变化，即为大气压强的余压，它相当于在大气压强上的叠加一个声波扰动引起的压强变化。由于声压的测量比较容易实现，通过声压的测量也可以间接求得质点速度等其它物理量，所以声学中常用这个物理量来描述声波

我们知道大气压强单位 1Pa = 1 pascal = 1N/m

实际计算可以参考http://www.sengpielaudio.com/calculator-soundlevel.htm

由于人对不同的声音频段 听感大小不一致，所以要对声音进行计权处理

如下实现 A  B   C 计权的实现，计权的实现参考标准，用于逼近实际的等响度曲线

 

ISO 226-2003标准

A、B、C三种计权网络特性，分别对应于倒置的40、70、100Phon等响曲线（1000Hz归一化到0dB），其作用是分别反应人耳对低、中、高声压级的响度感觉。A计权被证实是人耳对声压级主观反应的极好校正。对由A计权测量的声级称为A声级，记作LPA 或dB(A)。近来B计权、C计权已很少采用。

• A计权：40Phon等响曲线的翻转，模拟55dB以下低强度噪声特性。

• B计权：70Phon等响曲线的翻转，模拟55~85dB中等强度噪声特性。

• C计权：100Phon等响曲线的翻转，模拟高强度噪声特性。

• D计权：专用于飞机噪声的测量。

target_folder='audio/'
audio_targets = '.wav'
spl_folder = '/c_audio'
from  librosa import load
from  os import  listdir,path
from scipy.signal import lfilter,bilinear
from numpy import pi, convolve,log10,sqrt,sum,power
from csv import writerdef a_weighting_coeffs_design(sample_rate):"""Returns b and a coeff of a A-weighting filter.Parameters----------sample_rate : scalarSample rate of the signals that well be filtered.Returns-------b, a : ndarrayFilter coefficients for a digital weighting filter.Examples-------->>> b, a = a_weighting_coeff_design(sample_rate)To Filter a signal use scipy lfilter:>>> from scipy.signal import lfilter>>> y = lfilter(b, a, x)See Also--------b_weighting_coeffs_design : B-Weighting coefficients.c_weighting_coeffs_design : C-Weighting coefficients.weight_signal : Apply a weighting filter to a signal.scipy.lfilter : Filtering signal with `b` and `a` coefficients."""f1 = 20.598997f2 = 107.65265f3 = 737.86223f4 = 12194.217A1000 = 1.9997numerators = [(2 * pi * f4)**2 * (10**(A1000 / 20.0)), 0., 0., 0., 0.]denominators = convolve([1., +4 * pi * f4, (2 * pi * f4)**2],[1., +4 * pi * f1, (2 * pi * f1)**2])denominators = convolve(convolve(denominators, [1., 2 * pi * f3]),[1., 2 * pi * f2])return bilinear(numerators, denominators, sample_rate)def b_weighting_coeffs_design(sample_rate):"""Returns `b` and `a` coeff of a B-weighting filter.B-Weighting is no longer described in DIN61672.Parameters----------sample_rate : scalarSample rate of the signals that well be filtered.Returns-------b, a : ndarrayFilter coefficients for a digital weighting filter.Examples-------->>> b, a = b_weighting_coeff_design(sample_rate)To Filter a signal use :function: scipy.lfilter:>>> from scipy.signal import lfilter>>> y = lfilter(b, a, x)See Also--------a_weighting_coeffs_design : A-Weighting coefficients.c_weighting_coeffs_design : C-Weighting coefficients.weight_signal : Apply a weighting filter to a signal."""f1 = 20.598997f2 = 158.5f4 = 12194.217B1000 = 0.17numerators = [(2 * pi * f4)**2 * (10**(B1000 / 20)), 0, 0, 0]denominators = convolve([1, +4 * pi * f4, (2 * pi * f4)**2],[1, +4 * pi * f1, (2 * pi * f1)**2])denominators = convolve(denominators, [1, 2 * pi * f2])return bilinear(numerators, denominators, sample_rate)def c_weighting_coeffs_design(sample_rate):"""Returns b and a coeff of a C-weighting filter.Parameters----------sample_rate : scalarSample rate of the signals that well be filtered.Returns-------b, a : ndarrayFilter coefficients for a digital weighting filter.Examples--------b, a = c_weighting_coeffs_design(sample_rate)To Filter a signal use scipy lfilter:from scipy.signal import lfiltery = lfilter(b, a, x)See Also--------a_weighting_coeffs_design : A-Weighting coefficients.b_weighting_coeffs_design : B-Weighting coefficients.weight_signal : Apply a weighting filter to a signal."""f1 = 20.598997f4 = 12194.217C1000 = 0.0619numerators = [(2 * pi * f4)**2 * (10**(C1000 / 20)), 0, 0]denominators = convolve([1, +4 * pi * f4, (2 * pi * f4)**2],[1, +4 * pi * f1, (2 * pi * f1)**2])return bilinear(numerators, denominators, sample_rate)def SPLCal(x):Leng = len(x)pa = sqrt(sum(power(x, 2))/Leng)p0 = 2e-5spl = 20 * log10(pa / p0)return spl    def preprocess_spl(name,spl):"""Main logic for SPL weighting"""n = 1##at = find_recordings(target_folder, audio_targets)at =listdir(target_folder)for f in at:filename = path.join(target_folder, f)x, Fs = load(filename)b, a = c_weighting_coeffs_design(Fs)y = lfilter(b, a, x)out = SPLCal(y)spl.append(out)name.append(f[:-4])print(filename[6:-4]+"     spl:"+str(out))'''print("--- Preprocessing SPLs: " + str(round(n / len(at) * 100, 2)) +"% done. ---\t\t",end='\r\r\r\n\n')'''n += 1    
if __name__ == '__main__':name =[]spl= []preprocess_spl(name,spl)header =['name', 'spl(dbc)']with open('save.csv', 'w') as file:# 2. Create a CSV writermywrite = writer(file)# 3. Write data to the filemywrite.writerow(header)tmp = zip(name,spl)mywrite.writerows(tmp)file.close()