This code appears to implement a function for performing frame-based...

This code appears to implement a function for performing frame-based analysis of an audio signal in the context of a neural network-based denoising system. Here's what it does step-by-step:

Inputs and Outputs

• Inputs:

  1. DenoiseState *st: A pointer to the state structure containing the internal state for denoising, including memory buffers like the previous frame's data (analysis_mem).
  2. kiss_fft_cpx *X: Pointer to output frequency-domain data after an FFT transform.
  3. float *Ex: Pointer to output energy values for different frequency bands.
  4. const float *in: Pointer to the input time-domain audio frame data.

• Outputs: Updates the frequency-domain representation of the input (X) and the band energy array (Ex), while also maintaining internal state.

Step-by-Step Breakdown:

1. Temporary Signal Buffer:

   • float x[WINDOW_SIZE]; declares a temporary buffer for holding a windowed audio frame.
   • RNN_COPY(x, st->analysis_mem, FRAME_SIZE); copies FRAME_SIZE samples from the previous input (stored in analysis_mem) into the beginning of the buffer x. This ensures continuity between frames.

2. Append Current Frame:

   • for (i=0; i<FRAME_SIZE; i++) x[FRAME_SIZE + i] = in[i]; adds the current input frame's samples (in) to the buffer x after the data copied from analysis_mem. This results in a buffer of size WINDOW_SIZE (the length of the analysis window).

3. Update Internal State:

   • RNN_COPY(st->analysis_mem, in, FRAME_SIZE); updates the state variable analysis_mem with the current input frame (in). This stores the current frame for continuity in the next iteration.

4. Apply Windowing:

   • apply_window(x); applies a windowing function to the buffer x. This step is likely used to minimize spectral artifacts caused by frame boundaries (e.g., by applying a Hamming or Hann window).

5. Transform into Frequency Domain:

   • forward_transform(X, x); performs a forward FFT (Fast Fourier Transform) on the windowed signal x, storing the result in the frequency-domain array X.

6. Frequency Range Limiting (Optional, Training Only):

   • In training mode (#if TRAINING is defined), it zeroes out the FFT coefficients above a certain cutoff frequency (lowpass) in the array X. This limits the analysis to a lower-frequency range, possibly to exclude high-frequency noise or simplify the training process:

     for (i=lowpass; i<FREQ_SIZE; i++)
       X[i].r = X[i].i = 0;
     

7. Compute Band Energy:

   • compute_band_energy(Ex, X); calculates the energy for each frequency band and stores the results in Ex. This step is critical for analyzing the spectral content of the input signal and is probably used for features in the RNN-based denoising model.

Summary:

The function rnn_frame_analysis processes an audio frame for analysis in an RNN-based denoising system. It:

1. Prepares a windowed buffer of overlapping frames.
2. Transforms the frame into the frequency domain.
3. Computes the energy in different frequency bands.
4. Maintains internal state (analysis_mem) for overlap-add operations between frames.
5. Optionally applies a low-pass filter to limit the frequency range during training.

This function is likely part of a larger system that uses the frequency and energy data (X and Ex) as input features for a neural network or other processing stages.