在python

Question

我正在尝试使用Python从.wav文件中计算频谱图。为此，我遵循in here上的说明。首先，我使用librosa库读取.wav文件。在链接中找到的代码可以正常工作。该代码是：

sig, rate = librosa.load(file, sr = None)
sig = buf_to_int(sig, n_bytes=2)
spectrogram = sig2spec(rate, sig)

和函数sig2spec：

def sig2spec(signal, sample_rate):

# Read the file.
# sample_rate, signal = scipy.io.wavfile.read(filename)
# signal = signal[0:int(1.5 * sample_rate)]  # Keep the first 3.5 seconds
# plt.plot(signal)
# plt.show()

# Pre-emphasis step: Amplification of the high frequencies (HF)
# (1) balance the frequency spectrum since HF usually have smaller magnitudes compared to LF
# (2) avoid numerical problems during the Fourier transform operation and
# (3) may also improve the Signal-to-Noise Ratio (SNR).
pre_emphasis = 0.97
emphasized_signal = numpy.append(signal[0], signal[1:] - pre_emphasis * signal[:-1])
# plt.plot(emphasized_signal)
# plt.show()
# Consequently, we split the signal into short time windows. We can safely make the assumption that
# an audio signal is stationary over a small short period of time. Those windows size are balanced from the
# parameter called frame_size, while the overlap between consecutive windows is controlled from the
# variable frame_stride.

frame_size = 0.025
frame_stride = 0.01
frame_length, frame_step = frame_size * sample_rate, frame_stride * sample_rate  # Convert from seconds to samples
signal_length = len(emphasized_signal)
frame_length = int(round(frame_length))
frame_step = int(round(frame_step))
num_frames = int(numpy.ceil(float(numpy.abs(signal_length - frame_length)) / frame_step))
# Make sure that we have at least 1 frame

pad_signal_length = num_frames * frame_step + frame_length
z = numpy.zeros((pad_signal_length - signal_length))
pad_signal = numpy.append(emphasized_signal, z)
# Pad Signal to make sure that all frames have equal
# number of samples without truncating any samples from the original signal

indices = numpy.tile(numpy.arange(0, frame_length), (num_frames, 1)) \
          + numpy.tile(numpy.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T

frames = pad_signal[indices.astype(numpy.int32, copy=False)]

# Apply hamming windows. The rationale behind that is  the assumption made by the FFT that the data
# is infinite and to reduce spectral leakage.
frames *= numpy.hamming(frame_length)

# Fourier-Transform and Power Spectrum
nfft = 2048
mag_frames = numpy.absolute(numpy.fft.rfft(frames, nfft))  # Magnitude of the FFT
pow_frames = ((1.0 / nfft) * (mag_frames ** 2))  # Power Spectrum

# Transform the FFT to MEL scale
nfilt = 40
low_freq_mel = 0
high_freq_mel = (2595 * numpy.log10(1 + (sample_rate / 2) / 700))  # Convert Hz to Mel
mel_points = numpy.linspace(low_freq_mel, high_freq_mel, nfilt + 2)  # Equally spaced in Mel scale
hz_points = (700 * (10 ** (mel_points / 2595) - 1))  # Convert Mel to Hz
bin = numpy.floor((nfft + 1) * hz_points / sample_rate)

fbank = numpy.zeros((nfilt, int(numpy.floor(nfft / 2 + 1))))
for m in range(1, nfilt + 1):
    f_m_minus = int(bin[m - 1])  # left
    f_m = int(bin[m])  # center
    f_m_plus = int(bin[m + 1])  # right

    for k in range(f_m_minus, f_m):
        fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])
    for k in range(f_m, f_m_plus):
        fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])
filter_banks = numpy.dot(pow_frames, fbank.T)
filter_banks = numpy.where(filter_banks == 0, numpy.finfo(float).eps, filter_banks)  # Numerical Stability
filter_banks = 20 * numpy.log10(filter_banks)  # dB

return (filter_banks/ np.amax(filter_banks))*255

我可以生成如下图像：

但是，在某些情况下，我的频谱图看起来像：

正在发生某种真正的怪异现象，因为在信号开始时，图像中有些蓝色条纹我不明白，如果它们真的意味着某些东西或在计算频谱图时出现错误。我想这个问题与标准化有关，但是我不确定到底是什么。

编辑：我尝试使用库中推荐的librosa：

sig, rate = librosa.load("audio.wav", sr = None)
spectrogram = librosa.feature.melspectrogram(y=sig, sr=rate)
spec_shape = spectrogram.shape
fig = plt.figure(figsize=(spec_shape), dpi=5)
lidis.specshow(spectrogram.T, cmap=cm.jet)
plt.tight_layout()
plt.savefig("spec.jpg")

现在规范几乎到处都是深蓝色：

Answer 1

可能是因为您尚未调整librosa melspectrogram方法的参数。

在原始实现中，您指定nfft = 2048。可以将其传递给质谱图，您将看到不同的结果。

本文介绍“波形频率分辨率”和“ fft分辨率”，这是进行FT时的重要参数。了解它们可能有助于重现原始的频谱图。

http://www.bitweenie.com/listings/fft-zero-padding/

specshow方法也具有各种参数，这些参数将直接影响您生成的图。

此堆栈文章列出了MATLAB中的各种频谱参数，但是您可以在MATLAB版本和librosa版本之间绘制相似之处。

What is a spectrogram and how do I set its parameters?