在Javascript中使用FFT计算音频文件的平均幅度 [英] Calculating The Average Amplitude of an Audio File Using FFT in Javascript
问题描述
我目前正在参与一个项目，我想在该项目中找到任何给定AAC文件中给定音频数据的平均幅度.我目前正在将文件读取为数组缓冲区，并传递给Uint8Array.
I am currently involved in a project in which I want to find the average amplitude for given audio data in any given AAC file. I am currently reading the file as an array buffer and passing into an Uint8Array.
var dataArray = new Uint8Array(buffer)
然后，我建立了两个数组，一个是实数(包含音频数据)，另一个是虚数(包含全零)，然后将它们传递给FFT.然后，将音频数据放入新的数组中，以便不再将数组中的数字视为无符号的8位整数.
Then I set up two arrays, one real(containing the audio data) and one imaginary(containing all zeros), and pass them into an FFT. The audio data is then placed into a new array such that the numbers within the array are no longer treated as unsigned 8-bit integers.
var realArray = [audio data here]
var imagArray = [0,0,0,0,0,0,...]
transform(realArray, imagArray)
然后，我遍历数组，从0循环到N/2，其中N是包含原始音频数据的初始缓冲区的大小，并计算每个频点的大小.最后，我将这些大小的总和除以N/2.
I then go through the arrays, looping from 0 to N/2, where N is the size of the initial buffer containing the raw audio data, and calculate the magnitude of each frequency bin. Finally, I divide the sum of these magnitudes by N/2.
问题在于，在某些情况下，与以较高强度播放的音频给出的值相比，对于以较低强度播放的音频，我得到的值较高.我的方法相对于实现目标是否正确，或者是否有更好的实现目标的方法?谢谢.
The problem is that on some occasions, for audio played at a lower intensity, I get a high value compared to the value given by audio played at a higher intensity. Is my approach correct in relation to achieving my goal or is there a better way of going about it? Thanks.
注意:对于感兴趣的人，可以在此处使用几种语言找到正在使用的FFT. FFT 我将音频文件的中间2 ^ 20字节传递给FFT，然后进行计算.
Note: For those interested the FFT being used can be found here in several languages. FFT I am passing the middle 2^20 bytes of the audio file into the FFT then doing my calculations.
推荐答案
您真的不需要为此使用FFT- Parseval定理本质上意味着时域中的能量等于频域中的能量，因此FFT部分是多余的-您可以在时域中计算幅度.通常，这是通过在选定的时间范围内(信号的长度)计算信号的 RMS值来完成的.这段时间取决于您要达到的目标.
You really don't need to use an FFT for this - Parseval's theorem essentially means that energy in the time domain is equal to energy in the frequency domain, so the FFT part is redundant - you can just calculate amplitude in the time domain. Typically this is done by calculating the RMS value of the signal over a chosen time window (the length of this time window depends on what you are trying to achieve).
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