If you need to convey a signal in the audio domain, playing on time but not in pitch: You have to know what your signal is composed of. So as to synthesitize the good frequency when its worth.
1/ You have all the parameters known, like in analogic synthesizing, you know you want to synthetize one note, so you tune all the Oscillators frequencies you can to this value: I guess this not what you can do, any virtual/virtual analog synth can do this on your demand.
2/ you have a source sound ou want to control You have to decompose it in items you can control to futhfill your harmonic constraint, in time and rhythmical constraints: 3 solutions.
a. FFT, fast fourrier transform, giving you the amount of power on all harmonics of your source sound, and up to you to enlarge the time scale of some harmonics or another ( really cook recipes, but really worth the expreriment)
b. Wavelet, close to FFT, but focussing on harmonic details whenever they happen, and how precise they happen. (imagine its like FFT optimizing on some meaningfull frequencies at each time)
c. Granular Synthesis, i think it is the easiest: it perfroms windows, (applying sort of Gauss Normal law to each time fragment of sound), like clouds of windows over your original sound, decoupling it in numerous parts, totally manageable on their pitch and duration (the speed and period of the window applied on the sound)
There maybe be a lot of other techniques but I am not aware of.
The basic idea is that you need to convert a signal along a time axis into a signal over time and frequency axes. Then you modify that signal appropriately, then convert back again.
Windowed fast fourier transforms are a common approach - take a short segment of the signal, convert to the frequency domain, repeat for periodic steps through the signal. Modifying the signal basically means relabelling your frequency and/or time axis scaling before applying the inverse transforms. Windows will probably overlap a little, so you can blend (cross-fade) from one block to another.
Another possible approach is to use wavelet transforms, filter banks, or some other closely related multi-resolution approach. The basis of these is the use of integral transforms in which each frequency is treated on an appropriate scale (relative to wavelength). A morlet basis, for example, is very like a single-wavelength-limited variation of the sine+j.cosine combination that is the basis of the fourier transform.
In theory, these should provide a better result. As the transforms naturally have both time and frequency axes, there is no need to generate the time axis "artificially" by windowing. This may avoid the sometimes obvious crossfade-between-blocks issues with the windowed Fourier transform approach. I'm going to guess that there may be other artefacts instead, but I don't know enough to know what they are.
Sorry if my terminology is misleading or wrong about multi-resolution stuff - I'm very far from being an expert.
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如果您需要在音频域中传送信号，按时播放但不按音调播放：
您必须知道您的信号由什么组成。从而在有价值的时候合成出好的频率。
1/您拥有已知的所有参数，就像在模拟合成中一样，您知道您想要合成一个音符，因此您可以将所有振荡器频率调整为该值：我想这不是您可以做的，任何虚拟/虚拟模拟合成器可以根据您的需求做到这一点。
2/您有一个想要控制的声音源
你必须将其分解为你可以控制的项目，以在时间和节奏约束下满足你的和声约束：3 种解决方案。
一个。 FFT，快速傅里叶变换，为您提供源声音的所有谐波的功率量，并由您决定放大某些谐波或其他谐波的时间尺度（确实很烹饪食谱，但确实值得实验）
b。小波，接近 FFT，但专注于谐波细节，无论它们何时发生，以及它们发生的精确程度。 （想象一下，它就像每次在一些有意义的频率上进行 FFT 优化
） 粒度合成，我认为这是最简单的：它执行窗口（对声音的每个时间片段应用某种高斯正常定律），就像原始声音上的窗口云一样，将其解耦为多个部分，完全可以控制它们的音调和持续时间（应用于声音的窗口的速度和周期）
可能还有很多其他技术，但我不知道。
If you need to convey a signal in the audio domain, playing on time but not in pitch:
You have to know what your signal is composed of. So as to synthesitize the good frequency when its worth.
1/ You have all the parameters known, like in analogic synthesizing, you know you want to synthetize one note, so you tune all the Oscillators frequencies you can to this value: I guess this not what you can do, any virtual/virtual analog synth can do this on your demand.
2/ you have a source sound ou want to control
You have to decompose it in items you can control to futhfill your harmonic constraint, in time and rhythmical constraints: 3 solutions.
a. FFT, fast fourrier transform, giving you the amount of power on all harmonics of your source sound, and up to you to enlarge the time scale of some harmonics or another ( really cook recipes, but really worth the expreriment)
b. Wavelet, close to FFT, but focussing on harmonic details whenever they happen, and how precise they happen. (imagine its like FFT optimizing on some meaningfull frequencies at each time)
c. Granular Synthesis, i think it is the easiest: it perfroms windows, (applying sort of Gauss Normal law to each time fragment of sound), like clouds of windows over your original sound, decoupling it in numerous parts, totally manageable on their pitch and duration (the speed and period of the window applied on the sound)
There maybe be a lot of other techniques but I am not aware of.
关于音频时间刻度音调修改的维基百科文章可能会有所帮助。
The Wikipedia article on Audio timescale-pitch modification may be helpful.
基本思想是，您需要将沿时间轴的信号转换为沿时间和频率轴的信号。然后，您适当地修改该信号，然后再次转换回来。
加窗快速傅里叶变换是一种常见的方法 - 获取一小段信号，转换到频域，在信号中重复周期性步骤。修改信号基本上意味着在应用逆变换之前重新标记频率和/或时间轴缩放。窗口可能会重叠一点，因此您可以从一个块混合（交叉淡入淡出）到另一个块。
另一种可能的方法是使用小波变换、滤波器组或其他一些密切相关的多分辨率方法。这些的基础是积分变换的使用，其中每个频率都以适当的比例（相对于波长）进行处理。例如，莫雷基非常类似于作为傅立叶变换基础的正弦+j余弦组合的单波长限制变体。
理论上，这些应该会提供更好的结果。由于变换自然具有时间轴和频率轴，因此不需要通过加窗“人为”生成时间轴。这可以避免窗口傅立叶变换方法有时明显的块间交叉淡入淡出问题。我猜测可能还有其他文物，但我还不够了解它们是什么。
如果我的术语在多分辨率方面有误导性或错误，我很抱歉 - 我距离专家还很远。
The basic idea is that you need to convert a signal along a time axis into a signal over time and frequency axes. Then you modify that signal appropriately, then convert back again.
Windowed fast fourier transforms are a common approach - take a short segment of the signal, convert to the frequency domain, repeat for periodic steps through the signal. Modifying the signal basically means relabelling your frequency and/or time axis scaling before applying the inverse transforms. Windows will probably overlap a little, so you can blend (cross-fade) from one block to another.
Another possible approach is to use wavelet transforms, filter banks, or some other closely related multi-resolution approach. The basis of these is the use of integral transforms in which each frequency is treated on an appropriate scale (relative to wavelength). A morlet basis, for example, is very like a single-wavelength-limited variation of the sine+j.cosine combination that is the basis of the fourier transform.
In theory, these should provide a better result. As the transforms naturally have both time and frequency axes, there is no need to generate the time axis "artificially" by windowing. This may avoid the sometimes obvious crossfade-between-blocks issues with the windowed Fourier transform approach. I'm going to guess that there may be other artefacts instead, but I don't know enough to know what they are.
Sorry if my terminology is misleading or wrong about multi-resolution stuff - I'm very far from being an expert.