MIDI does store musical scores, but it doesn't (normally) extract them from recorded sounds. You can't take an mp3 file and "convert it to MIDI", in a standard or entirely reliable way.
You create a MIDI file using a recorder (or "sequencer"), which might be a desktop application where you "write the score" like a composer does, or it might be a musical device like a keyboard, which records which keys you press, how hard and for how long, and interprets that as a score.
A MIDI player takes the data/score, and reproduces it using its own voice (or "sound font" if you like). So the advantage of MIDI data is firstly that the voice is already available on the playback device (and so the data is very compact), and second that the same data ("tune") can be played using different voices ("instruments")[*].
I believe there are MIDI guitars, but I don't know how "good" they are. The tone of an electric guitar comes in part from resonances of the solid body. This could of course be imitated by the voice at playback time, but there are bound to be some things that you can do with an electric guitar but which the MIDI format cannot capture or represent (for example I'd guess feedback is impossible).
Software exists to extract MIDI data from recorded sound - this is a bit like the way OCR extracts ASCII character data from images of text. It's not a major means of recording someone's guitar-playing, but if what you want is to get a first approximation to the score/tabs, you could try it.
Here's a randomly-selected example, found by Googling "convert from wav to MIDI":
[*] But members of the audience, you find yourselves wondering, "what is this mindless automaton which bangs out the tunes it's instructed to, without comprehension or any aesthetic sense". Ladies and gentlemen, Colin Sell at the piano.
Music recognition/retrieval is an extremely difficult and almost entirely unsolved AI problem. Try to extract the frequency from a signal file of someone playing a single unwavering note one time - it's much more difficult than just "apply Fourier transform, read off solution". Compound that with polyphony, noise, rubato, vibrato/portamento, plus the fact that (contrary to speech recognition) we don't even have a working a-priori model of what music actually is, and you begin to see the difficulty. There are absolutely fascinating research papers and even entire conferences on the topic, but in the short term, you're just plain out of luck.
Are you aware you are attempting something extremely difficult? It's a very complex topic you could spend years researching yourself or pay $$$$$$ for existing commercial solutions.
MIDI is a reasonable choice for your output format.
For the rest you will need Fast Fourier Transforms working off a high-resolution capture of the input analogue sounds plus at least seven years of musical theory.
If the player is playing in tune, there will be very distinct frequencies in the signal, or at least frequencies with a mathematical separation. It may be possible to characterise a signal using spectral analysis to distinguish music from noise; or at least melodic music from noise - avant guarde experimental music may not pass ;). The distinction may become more difficult with multiple instrumentalists, percussion, and non-standard or poor tuning; traditional Chinese or Indian music for example uses different scales than western music.
Extracting the frequencies in the signal will require signal processing techniques such as Fast Fourier Transform. Categorising the signal as music/not music could be done by statistical analysis, or AI techniques such as neural networks or fuzzy logic
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MIDI 确实存储乐谱，但它（通常）不会从录制的声音中提取它们。您无法以标准或完全可靠的方式获取 mp3 文件并将其“转换为 MIDI”。
您使用录音机（或“音序器”）创建 MIDI 文件，它可能是一个桌面应用程序，您可以像作曲家一样“编写乐谱”，也可能是一个音乐设备，例如键盘，它记录您按下的键，有多难，持续多长时间，并将其解释为分数。
MIDI 播放器获取数据/乐谱，并使用自己的声音（或“声音字体”，如果您喜欢）再现它。因此，MIDI 数据的优点首先是声音已经可以在播放设备上使用（因此数据非常紧凑），其次可以使用不同的声音（“乐器”）来播放相同的数据（“曲调”）。 [*]。
我相信有 MIDI 吉他，但我不知道它们有多“好”。电吉他的音色部分来自于固体的共振。这当然可以通过播放时的声音来模仿，但肯定有一些事情你可以用电吉他来做，但 MIDI 格式无法捕获或表示（例如，我猜反馈是不可能的）。
有软件可以从录制的声音中提取 MIDI 数据 - 这有点像 OCR 从文本图像中提取 ASCII 字符数据的方式。它不是录制某人吉他演奏的主要方法，但如果您想要获得乐谱/乐谱的初步近似值，您可以尝试一下。
这是一个随机选择的示例，通过 Google 搜索“从 wav 转换为 MIDI”找到：
http://www.pluto.dti.ne.jp/~araki/amazingmidi/
[*]但是观众们，你们会发现自己想知道，“这个无意识的自动机是什么，它会发出它所指示的曲调没有理解力或任何审美意识”。女士们先生们，科林·塞尔正在弹钢琴。
MIDI does store musical scores, but it doesn't (normally) extract them from recorded sounds. You can't take an mp3 file and "convert it to MIDI", in a standard or entirely reliable way.
You create a MIDI file using a recorder (or "sequencer"), which might be a desktop application where you "write the score" like a composer does, or it might be a musical device like a keyboard, which records which keys you press, how hard and for how long, and interprets that as a score.
A MIDI player takes the data/score, and reproduces it using its own voice (or "sound font" if you like). So the advantage of MIDI data is firstly that the voice is already available on the playback device (and so the data is very compact), and second that the same data ("tune") can be played using different voices ("instruments")[*].
I believe there are MIDI guitars, but I don't know how "good" they are. The tone of an electric guitar comes in part from resonances of the solid body. This could of course be imitated by the voice at playback time, but there are bound to be some things that you can do with an electric guitar but which the MIDI format cannot capture or represent (for example I'd guess feedback is impossible).
Software exists to extract MIDI data from recorded sound - this is a bit like the way OCR extracts ASCII character data from images of text. It's not a major means of recording someone's guitar-playing, but if what you want is to get a first approximation to the score/tabs, you could try it.
Here's a randomly-selected example, found by Googling "convert from wav to MIDI":
http://www.pluto.dti.ne.jp/~araki/amazingmidi/
[*] But members of the audience, you find yourselves wondering, "what is this mindless automaton which bangs out the tunes it's instructed to, without comprehension or any aesthetic sense". Ladies and gentlemen, Colin Sell at the piano.
音乐识别/检索是一个极其困难且几乎完全未解决的人工智能问题。尝试从某人一次演奏单个坚定音符的信号文件中提取频率 - 这比仅仅“应用傅里叶变换，读出解决方案”要困难得多。再加上复调、噪音、自由节奏、颤音/滑音，再加上（与语音识别相反）我们甚至没有一个关于音乐实际是什么的工作先验模型，你就会开始看到困难。关于这个主题有绝对令人着迷的研究论文，甚至整个会议，但从短期来看，你只是运气不好。
Music recognition/retrieval is an extremely difficult and almost entirely unsolved AI problem. Try to extract the frequency from a signal file of someone playing a single unwavering note one time - it's much more difficult than just "apply Fourier transform, read off solution". Compound that with polyphony, noise, rubato, vibrato/portamento, plus the fact that (contrary to speech recognition) we don't even have a working a-priori model of what music actually is, and you begin to see the difficulty. There are absolutely fascinating research papers and even entire conferences on the topic, but in the short term, you're just plain out of luck.
你知道你正在尝试一些极其困难的事情吗？这是一个非常复杂的主题，您可能需要花费数年时间自行研究，或者为现有的商业解决方案支付 $$$$$$ 费用。
Are you aware you are attempting something extremely difficult? It's a very complex topic you could spend years researching yourself or pay $$$$$$ for existing commercial solutions.
MIDI 是您输出格式的合理选择。
其余的，您将需要快速傅里叶变换来高分辨率捕获输入模拟声音，再加上至少七年的音乐理论。
祝你好运。
MIDI is a reasonable choice for your output format.
For the rest you will need Fast Fourier Transforms working off a high-resolution capture of the input analogue sounds plus at least seven years of musical theory.
Good luck.
如果演奏者演奏得合拍，信号中就会有非常不同的频率，或者至少是具有数学分离的频率。可以使用频谱分析来表征信号，以区分音乐和噪声；或者至少来自噪音的旋律音乐 - 前卫实验音乐可能不会通过；）。如果有多个乐器演奏者、打击乐以及非标准或糟糕的调音，区分可能会变得更加困难；例如，传统的中国或印度音乐使用的音阶与西方音乐不同。
提取信号中的频率需要诸如快速傅立叶变换之类的信号处理技术。将信号分类为音乐/非音乐可以通过统计分析或人工智能技术（例如神经网络或模糊逻辑）来完成
If the player is playing in tune, there will be very distinct frequencies in the signal, or at least frequencies with a mathematical separation. It may be possible to characterise a signal using spectral analysis to distinguish music from noise; or at least melodic music from noise - avant guarde experimental music may not pass ;). The distinction may become more difficult with multiple instrumentalists, percussion, and non-standard or poor tuning; traditional Chinese or Indian music for example uses different scales than western music.
Extracting the frequencies in the signal will require signal processing techniques such as Fast Fourier Transform. Categorising the signal as music/not music could be done by statistical analysis, or AI techniques such as neural networks or fuzzy logic