peak detection - Envelope reconstruction algorithm for audio processing

I need to reconstruct the envelope of a sound.

Audio data are professionally-recorded natural sounds (speech, bird songs) with very little noise. I would prefer working in the time domain rather than in the frequency domain (I've seen some algorithms based on FFT transformations that looked overcomplicated for what I need). The algorithm will be implemented in an interpreted language so it needs to stay "light" in computation.

As a first approach, I considered using a peak detection algorithm, then doing a linear interpolation between the peaks. But isn't there some pitfalls with such a naive approach? Are there some standard ways of implementing envelope reconstruction in the time domain that would better suit my needs?

_{FWIW, I'm not familiar with digital signal processing vocabulary, so do not hesitate to reword my question if I misused some terms}

Answer

As I said in a comment, you can get the envelope of a signal by running it through a lowpass filter.

The steps required for this (usually) are

1. 
   

   Go through all the samples (x(N) ) and check for negative samples. Convert them to positive values

   
   


2. 
   

   Implement a lowpass filter (FIR) by creating a filter kernel of appropriate length M (h(M) ). Note that your FIR filter (sin(x)/x) should normally be multiplied by a window function (e.g Hamming, Blackman etc).

   
   



3. 
   

   Convolve your signal with the filter kernel.

   
   

   for(int i=0;i
    for(int j=0;j
      y[i+j] = y[i+j]+x[i]*h(j); 
   

Note that the output (filtered) signal is usually right shifted by M samples. Also, M is often chosen to be odd in order to create a perfectly symmetrical filter although I'm sure not all implementations adhere to this.