Struct SignalProcessor 

pub struct SignalProcessor { /* private fields */ }

Signal processor for pre-processing sensor data

Implementations

impl SignalProcessor

pub fn new(buffer_size: usize) -> Self

Create a new signal processor

pub fn with_sample_rate(self, rate: f32) -> Self

Set the sample rate

pub fn buffer_size(&self) -> usize

Get the buffer size

pub fn sample_rate(&self) -> f32

Get the sample rate

pub fn fft(&mut self, signal: &Array1<f32>) -> IoResult<Vec<Complex<f32>>>

Apply FFT to signal

pub fn ifft(&mut self, spectrum: &mut [Complex<f32>]) -> IoResult<Array1<f32>>

Apply inverse FFT

pub fn fft_pow2(&mut self, signal: &Array1<f32>) -> IoResult<Vec<Complex<f32>>>

Optimized FFT for power-of-2 sizes

Uses specialized FFT planning for power-of-2 sizes which can be more efficient. Falls back to standard FFT for non-power-of-2 sizes.

pub fn ifft_pow2( &mut self, spectrum: &mut [Complex<f32>], ) -> IoResult<Array1<f32>>

Optimized inverse FFT for power-of-2 sizes

pub fn power_spectrum_pow2( &mut self, signal: &Array1<f32>, ) -> IoResult<Vec<f32>>

Compute power spectrum efficiently for power-of-2 sizes

Returns the magnitude squared of the FFT.

pub fn zero_pad_pow2(signal: &Array1<f32>) -> Array1<f32>

Zero-pad signal to next power of 2 for optimal FFT performance

pub fn apply_filter( &mut self, signal: &Array1<f32>, filter: Filter, ) -> IoResult<Array1<f32>>

Apply a filter to the signal

pub fn power_spectrum(&mut self, signal: &Array1<f32>) -> IoResult<Array1<f32>>

Compute power spectrum (magnitude squared)

pub fn magnitude_spectrum( &mut self, signal: &Array1<f32>, ) -> IoResult<Array1<f32>>

Compute magnitude spectrum

pub fn phase_spectrum(&mut self, signal: &Array1<f32>) -> IoResult<Array1<f32>>

Compute phase spectrum

pub fn zero_crossings(signal: &Array1<f32>) -> usize

Compute zero-crossings count

pub fn peak_to_peak(signal: &Array1<f32>) -> f32

Compute peak-to-peak amplitude

pub fn remove_dc(signal: &Array1<f32>) -> Array1<f32>

Apply DC removal (subtract mean)

pub fn envelope(&mut self, signal: &Array1<f32>) -> IoResult<Array1<f32>>

Apply envelope detection via Hilbert transform approximation

pub fn normalize(signal: &Array1<f32>) -> Array1<f32>

Normalize signal to [-1, 1] range

pub fn rms(signal: &Array1<f32>) -> f32

Compute RMS (Root Mean Square) of signal

pub fn spectrogram( &mut self, signal: &Array1<f32>, n_fft: usize, hop_length: usize, window: WindowType, ) -> IoResult<Spectrogram>

Compute spectrogram (Short-Time Fourier Transform)

Returns a 2D array where rows are time frames and columns are frequency bins. Only the positive frequency bins (0 to n_fft/2) are returned.

pub fn create_window(window_type: WindowType, size: usize) -> Vec<f32>

Create a window function

pub fn apply_window_to_frame(frame: &[f32], window_type: WindowType) -> Vec<f32>

Apply window function to a frame (convenience method)

pub fn mel_filterbank( num_filters: usize, n_fft: usize, sample_rate: f32, f_min: f32, f_max: f32, ) -> Vec<Vec<f32>>

Compute Mel filterbank

pub fn hz_to_mel(hz: f32) -> f32

Convert frequency in Hz to Mel scale

pub fn mel_to_hz(mel: f32) -> f32

Convert Mel scale to frequency in Hz

pub fn mfcc( &mut self, signal: &Array1<f32>, n_mfcc: usize, n_fft: usize, hop_length: usize, n_mels: usize, ) -> IoResult<Vec<Vec<f32>>>

Compute Mel-frequency cepstral coefficients (MFCCs)

Trait Implementations

impl Debug for SignalProcessor

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.