在Python中，可以使用`matplotlib`库来绘制频谱图，以下是几种不同场景下绘制频谱图的方法： ### 结合折线图绘制频谱图 通过`pyplot.subplots()`创建`Axes`对象，`Axes1`对象调用`plot()`绘制折线图，`Axes2`对象调用`specgram()`方法绘制频谱图。示例代码如下： ```python import numpy as np import matplotlib.pyplot as plt fig, (ax1, ax2) = plt.subplots(nrows=2) dt = 0.0005 t = np.arange(0.0, 20.0, dt) s1 = np.cos(2 * np.pi * 100 * t) s2 = 2 * np.cos(2 * np.pi * 400 * t) s2[t <= 5] = s2[12 < t] = 0 nse = 0.01 * np.random.random(size=len(t)) x = s1 + s2 + nse ax1.plot(t, x, "pink") ax2.specgram(x, NFFT=1024, Fs=1, noverlap=900, cmap="gray") plt.show() ``` 上述代码展示了如何同时绘制折线图和频谱图，首先创建了两个子图，然后分别在两个子图上绘制折线图和频谱图 [^1]。 ### 绘制语音信号的频谱图 需要读取`.wav`文件，对数据进行处理后使用`plt.specgram()`绘制频谱图。示例代码如下： ```python import numpy as np import matplotlib.pyplot as plt import os import wave f = wave.open('filename.wav', 'rb') params = f.getparams() nchannels, sampwidth, framerate, nframes = params[:4] strData = f.readframes(nframes) waveData = np.fromstring(strData, dtype=np.short) waveData = waveData * 1.0 / max(abs(waveData)) waveData = np.reshape(waveData, [nframes, nchannels]).T f.close() framelength = 0.025 framesize = framelength * framerate nfftdict = {} lists = [32, 64, 128, 256, 512, 1024] for i in lists: nfftdict[i] = abs(framesize - i) sortlist = sorted(nfftdict.items(), key=lambda x: x[1]) framesize = int(sortlist[0][0]) NFFT = framesize overlapSize = 1.0 / 3 * framesize overlapSize = int(round(overlapSize)) spectrum, freqs, ts, fig = plt.specgram(waveData[0], NFFT=NFFT, Fs=framerate, window=np.hanning(M=framesize), noverlap=overlapSize, mode='default', scale_by_freq=True, sides='default', scale='dB', xextent=None) plt.ylabel('Frequency') plt.xlabel('Time') plt.title("name") plt.show() ``` 该代码读取一个`.wav`文件，对语音数据进行处理，然后计算合适的帧大小和重叠大小，最后使用`plt.specgram()`绘制频谱图 [^4]。