(8.D.2.5) CorrelationFract32
- David Buzzell (Unlicensed)
Overview
Computes the zero-lag cross correlation
Discussion
This module calculates the cross correlation of 2 single-channel inputs. The calcuation is block based and the module outputs a single value which is the correlation for the block of input data. The cross correlation is without time lag. The formula is:
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norm(in1,NORM_TYPE) * norm(in2,NORM_TYPE)
NORM_TYPE (Tunable parameter) chooses between L1 norm or L2 norm. L1 norm is the Manhattan distance of a vector. L2 norm (Euclidean) is the Pythagorean distance of a vector.
Type Definition
typedef struct _ModuleCorrelationFract32
{
ModuleInstanceDescriptor instance; // Common Audio Weaver module instance structure
INT32 norm; // Type of norm: L1_norm=1, L2_norm=2
} ModuleCorrelationFract32Class;Variables
Properties
Name | Type | Usage | isHidden | Default value | Range | Units |
norm | int | parameter | 0 | 2 | 1:2 | Â |
Pins
Input Pins
Name: in1
Description: Signal input 1
Data type: fract32
Channel range: 1
Block size range: Unrestricted
Sample rate range: Unrestricted
Complex support: Real
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Name: in2
Description: Signal input 2
Data type: fract32
Channel range: 1
Block size range: Unrestricted
Sample rate range: Unrestricted
Complex support: Real
Output Pins
Name: out
Description: xcorrelation value
Data type: fract32
MATLAB Usage
File Name: correlation_fract32_module.m
M=correlation_fract32_module(NAME)
This module calculates the cross correlation of 2 single-channel inputs.
The cross correlation is without time lag.
The formula is:
-----------------------------------------------
norm(in1,NORM_TYPE) * norm(in2,NORM_TYPE)
NORM_TYPE (Tunable parameter) chooses between L1 norm or L2 norm.
L1 norm is the 'manhattan' distance of a vector
L2 norm (Euclidean) is the Pythagorean distance of a vector
Arguments:
NAME - name of the module.Â