今天回歸到大綱中，關於音樂的部分。我標題通常喜歡生動一點詞彙來表達，以專業的口吻就是資料前處理、資料視覺化、資料探勘。

首先要有辦法處理音訊檔案，我們可以使用pydub。
但比較麻煩的是如果在自己的電腦，需要有ffmpeg 才能匯入mp3等格式，不然就只能用wav檔，
但如果在Colab上就直接有ffmpeg 能使用。範例如下，匯入後，用list的運算就能切片、串接、重覆等，效果上用加減值就能直接調大調小音量，也能淡入淡出和轉場，此外還能倒著放!

from pydub import AudioSegment

song = AudioSegment.from_wav("Maple_Leaf.wav")

#Slice
first_10_seconds = song[:10 * 1000]
last_5_seconds = song[-5 * 1000:]

# boost volume by 6dB
beginning = first_10_seconds + 6

# reduce volume by 3dB
end = last_5_seconds - 3

#Concatenate audio (add one file to the end of another)
without_the_middle = beginning + end

# 1.5 second crossfade
with_style = beginning.append(end, crossfade=1500)

# repeat the clip twice
do_it_over = with_style * 2

# 2 sec fade in, 3 sec fade out
awesome = do_it_over.fade_in(2000).fade_out(3000)
awesome.export("awesome.wav", format="wav")

backwards = song.reverse()
backwards.export("backwards.wav", format="wav")

波紋的部分，在stackoverflow裡就有解答，但要用第二個，第一個在下面討論串中也有人提出我遇到的error的問題，第二個已經包好成function直接可以拿來用：

import numpy as np
from matplotlib import pyplot as plt
import scipy.io.wavfile as wav
from numpy.lib import stride_tricks

""" short time fourier transform of audio signal """
def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
    win = window(frameSize)
    hopSize = int(frameSize - np.floor(overlapFac * frameSize))

    # zeros at beginning (thus center of 1st window should be for sample nr. 0)   
    samples = np.append(np.zeros(int(np.floor(frameSize/2.0))), sig)    
    # cols for windowing
    cols = np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1
    # zeros at end (thus samples can be fully covered by frames)
    samples = np.append(samples, np.zeros(frameSize))

    frames = stride_tricks.as_strided(samples, shape=(int(cols), frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy()
    frames *= win

    return np.fft.rfft(frames)    

""" scale frequency axis logarithmically """    
def logscale_spec(spec, sr=44100, factor=20.):
    timebins, freqbins = np.shape(spec)

    scale = np.linspace(0, 1, freqbins) ** factor
    scale *= (freqbins-1)/max(scale)
    scale = np.unique(np.round(scale))

    # create spectrogram with new freq bins
    newspec = np.complex128(np.zeros([timebins, len(scale)]))
    for i in range(0, len(scale)):        
        if i == len(scale)-1:
            newspec[:,i] = np.sum(spec[:,int(scale[i]):], axis=1)
        else:        
            newspec[:,i] = np.sum(spec[:,int(scale[i]):int(scale[i+1])], axis=1)

    # list center freq of bins
    allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
    freqs = []
    for i in range(0, len(scale)):
        if i == len(scale)-1:
            freqs += [np.mean(allfreqs[int(scale[i]):])]
        else:
            freqs += [np.mean(allfreqs[int(scale[i]):int(scale[i+1])])]

    return newspec, freqs

""" plot spectrogram"""
def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
    samplerate, samples = wav.read(audiopath)

    s = stft(samples, binsize)

    sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)

    ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel

    timebins, freqbins = np.shape(ims)

    print("timebins: ", timebins)
    print("freqbins: ", freqbins)

    plt.figure(figsize=(15, 7.5))
    plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="none")
    plt.colorbar()

    plt.xlabel("time (s)")
    plt.ylabel("frequency (hz)")
    plt.xlim([0, timebins-1])
    plt.ylim([0, freqbins])

    xlocs = np.float32(np.linspace(0, timebins-1, 5))
    plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
    ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
    plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])

    if plotpath:
        plt.savefig(plotpath, bbox_inches="tight")
    else:
        plt.show()

    plt.clf()

    return ims

ims = plotstft('Maple_Leaf.wav')

這是周杰倫的歌曲-楓的結果：

omnizart是個可以採譜的library，雖然我不是音樂家，但這聽起來很酷吧。所謂採譜就是它可以將輸入分離出鋼琴、鼓聲、人聲、和弦和節拍等。這是一個有發表paper的作品，雖然它有Colab環境，但我跑的結果是有error的，可以直接看說明文件中的範例。