## Posted tagged ‘C’

### Second part of tone detection using TKEO

October 20, 2012

It’s been almost a year since I put up my last tutorial about tone detection using the Teager Kaiser Energy Operator.  I have finally written something up for part two here.  Life with a young family has been quite busy for me.  If there is enough interest in the TKEO stuff, I may write a part 3 showing a fixed point version (another year from now?).

Let me know if there are any problems with formatting in the code (something that always seems to be a problem). Copying the code is easy.  If you put your mouse pointer over any of the code listings, a number of symbols will be shown at the top right.  One of these will allow the code to be copied to the clipboard.

### Implementation of FIR Filtering in C (Part 2)

October 8, 2009

In Part 1 I showed how to code a FIR filter in C using floating point. In this lesson I will show how to do the same thing using fixed point operations. The code example below will demonstrate the application of fixed point multiplication, rounding and saturation. The code has definitions for the FIR filtering function, followed by an example test program.

firFixed

And here is the code example:


#include <stdio.h>
#include <stdint.h>

//////////////////////////////////////////////////////////////
//  Filter Code Definitions
//////////////////////////////////////////////////////////////

// maximum number of inputs that can be handled
// in one function call
#define MAX_INPUT_LEN   80
// maximum length of filter than can be handled
#define MAX_FLT_LEN     63
// buffer to hold all of the input samples
#define BUFFER_LEN      (MAX_FLT_LEN - 1 + MAX_INPUT_LEN)

// array to hold input samples
int16_t insamp[ BUFFER_LEN ];

// FIR init
void firFixedInit( void )
{
memset( insamp, 0, sizeof( insamp ) );
}

// the FIR filter function
void firFixed( int16_t *coeffs, int16_t *input, int16_t *output,
int length, int filterLength )
{
int32_t acc;     // accumulator for MACs
int16_t *coeffp; // pointer to coefficients
int16_t *inputp; // pointer to input samples
int n;
int k;

// put the new samples at the high end of the buffer
memcpy( &insamp[filterLength - 1], input,
length * sizeof(int16_t) );

// apply the filter to each input sample
for ( n = 0; n < length; n++ ) {
// calculate output n
coeffp = coeffs;
inputp = &insamp[filterLength - 1 + n];
acc = 1 << 14;
// perform the multiply-accumulate
for ( k = 0; k < filterLength; k++ ) {
acc += (int32_t)(*coeffp++) * (int32_t)(*inputp--);
}
// saturate the result
if ( acc > 0x3fffffff ) {
acc = 0x3fffffff;
} else if ( acc < -0x40000000 ) {
acc = -0x40000000;
}
// convert from Q30 to Q15
output[n] = (int16_t)(acc >> 15);
}

// shift input samples back in time for next time
memmove( &insamp[0], &insamp[length],
(filterLength - 1) * sizeof(int16_t) );

}

//////////////////////////////////////////////////////////////
//  Test program
//////////////////////////////////////////////////////////////

// bandpass filter centred around 1000 Hz
// sampling rate = 8000 Hz
// gain at 1000 Hz is about 1.13

#define FILTER_LEN  63
int16_t coeffs[ FILTER_LEN ] =
{
-1468, 1058,   594,   287,    186,  284,   485,   613,
495,   90,  -435,  -762,   -615,   21,   821,  1269,
982,    9, -1132, -1721,  -1296,    1,  1445,  2136,
1570,    0, -1666, -2413,  -1735,   -2,  1770,  2512,
1770,   -2, -1735, -2413,  -1666,    0,  1570,  2136,
1445,    1, -1296, -1721,  -1132,    9,   982,  1269,
821,   21,  -615,  -762,   -435,   90,   495,   613,
485,  284,   186,   287,    594, 1058, -1468
};

// number of samples to read per loop
#define SAMPLES   80

int main( void )
{
int size;
int16_t input[SAMPLES];
int16_t output[SAMPLES];
FILE   *in_fid;
FILE   *out_fid;

// open the input waveform file
in_fid = fopen( "input.pcm", "rb" );
if ( in_fid == 0 ) {
printf("couldn't open input.pcm");
return;
}

// open the output waveform file
out_fid = fopen( "outputFixed.pcm", "wb" );
if ( out_fid == 0 ) {
printf("couldn't open outputFixed.pcm");
return;
}

// initialize the filter
firFixedInit();

// process all of the samples
do {
size = fread( input, sizeof(int16_t), SAMPLES, in_fid );
// perform the filtering
firFixed( coeffs, input, output, size, FILTER_LEN );
// write samples to file
fwrite( output, sizeof(int16_t), size, out_fid );
} while ( size != 0 );

fclose( in_fid );
fclose( out_fid );

return 0;
}



The first thing to notice is that the definitions for the input sample storage and handling are nearly the same as for the code in Part 1. The only difference is that the storage type is a 16 bit integer instead of double precision floating point.

The next difference is the inclusion of rounding in the calculation of each output. Rounding is used when converting the calculated result from a Q30 format number to Q15. Notice that I have loaded the rounding constant into the accumulator value (acc) at the beginning of the loop rather than adding it at the end. This is a small optimization commonly seen in code for FIR filters. If you are coding in assembly language, and your chip has a rounding instruction, it may be better to do the rounding at the end (depending on what the instruction actually does).

The multiplication itself is now an integer multiplication of two 16 bit values, each of which is a Q15 number. The accumulator variable is 32 bits, and holds a Q1.30 format number. There is one bit for the sign, one integer bit, and thirty fractional bits. Notice that I have cast each multiplier to a 32 bit value. Failure to do so should result in a 16 bit product and produce wrong results.

Next comes the overflow handling for converting from Q1.30 to Q30. The code checks for values beyond the limits of the largest/smallest Q30 number (no integer bits), and saturates if necessary.

Finally, a right shift by 15 is used to convert the MAC result from Q30 to Q15 and the result is stored to the output array.

The test program is simpler than the one in Part 1 because there is no need to convert the input and output samples to or from floating point. The most important thing to note is the change in the filter coefficient array. To generate these coefficients, I took the floating point coefficients from Part 1, multiplied by 32768, and then rounded to the nearest integer. The coefficients are in Q15 format, and note that none of the original floating point coefficients are close to one. Multiplying by 32768 would cause a problem for any coefficients larger than 32767/32768 or less than -1.

As in Part 1, the test input file should be 16 bit samples at a sampling rate of 8000 Hz. Try using a 1000 Hz tone with noise added to it. The bandpass filter should remove a large portion of the noise.

The filter has greater than unity gain at 1000 Hz and the gain is about 1.13. Admittedly it is not a great filter design, but it tests the saturation code if a full scale 1000 Hz sine wave is used as an input. Try a full scale 1000 Hz tone input with and without the saturation check and see what the difference is. There should be a small amount of distortion with the saturation code present (due to slight flattening of tone) and a large amount of distortion without it (due to overflow).

A fixed point FIR filter is fairly easy to implement. Personally I find the sample storage and movement to be the most difficult part. In Part 3 of this lesson I will demonstrate how to run multiple FIR filters on the same input.

### Implementation of FIR Filtering in C (Part 1)

October 1, 2009

In this lesson I will show how to code a finite impulse response (FIR) digital filter in the C programming language using floating point operations. Understanding the basic FIR filter is a stepping stone to understanding more complicated filters such as polyphase FIR filters and adaptive FIR filters.

A FIR filter is one in which the output depends only on the inputs (and not on previous outputs). The process is a discrete-time convolution between the input signal and the impulse response of the filter to apply. I will call this impulse response “the filter coefficients.” The following equivalent equations describe the convolution process:

equation 1: $y(n)=\sum_{k=0}^{N-1}h(k)x(n-k)$

equation 2: $y(n)=\sum_{k=n-(N-1)}^{n}h(n-k)x(k)$

where h is the impulse response, x is the input, y[n] is the output at time n and N is the length of the impulse response (the number of filter coefficients). For each output calculated, there are N multiply-accumulate operations (MACs). There is a choice of going through the filter coefficients forward and the input backwards (equation 1), or through the filter coefficients backwards and the input forwards (equation 2). In the example that follows, I will use the form in equation 1.

In my code example, samples will be stored in a working array, with lower indices being further back in time (at the “low” end). The input samples will be arranged in an array long enough to apply all of the filter coefficients. The length of the array required to process one input sample is N (the number of coefficients in the filter). The length required for M input samples is N – 1 + M.

A function will be defined that filters several input samples at a time. For example, a realtime system for processing telephony quality speech might process eighty samples at a time. This is done to improve the efficiency of the processing, since the overhead of calling the function is eighty times smaller than if it were called for every single sample. The drawback is that additional delay is introduced in the output.

The basic steps for applying a FIR filter are the following:

1. Arrange the new samples at the high end of the input sample buffer.

2. Loop through an outer loop that produces each output sample.

3. Loop through an inner loop that multiplies each filter coefficient by an input sample and adds to a running sum.

4. Shift the previous input samples back in time by the number of new samples that were just processed.

The code example defines an initialization function and a filtering function, and includes a test program. Download the PDF from the following link to see the code, or view it in-line below:

firFloat

#include <stdio.h>
#include <stdint.h>

//////////////////////////////////////////////////////////////
//  Filter Code Definitions
//////////////////////////////////////////////////////////////

// maximum number of inputs that can be handled
// in one function call
#define MAX_INPUT_LEN   80
// maximum length of filter than can be handled
#define MAX_FLT_LEN     63
// buffer to hold all of the input samples
#define BUFFER_LEN      (MAX_FLT_LEN - 1 + MAX_INPUT_LEN)

// array to hold input samples
double insamp[ BUFFER_LEN ];

// FIR init
void firFloatInit( void )
{
memset( insamp, 0, sizeof( insamp ) );
}

// the FIR filter function
void firFloat( double *coeffs, double *input, double *output,
int length, int filterLength )
{
double acc;     // accumulator for MACs
double *coeffp; // pointer to coefficients
double *inputp; // pointer to input samples
int n;
int k;

// put the new samples at the high end of the buffer
memcpy( &insamp[filterLength - 1], input,
length * sizeof(double) );

// apply the filter to each input sample
for ( n = 0; n < length; n++ ) {
// calculate output n
coeffp = coeffs;
inputp = &insamp[filterLength - 1 + n];
acc = 0;
for ( k = 0; k < filterLength; k++ ) {
acc += (*coeffp++) * (*inputp--);
}
output[n] = acc;
}
// shift input samples back in time for next time
memmove( &insamp[0], &insamp[length],
(filterLength - 1) * sizeof(double) );

}

//////////////////////////////////////////////////////////////
//  Test program
//////////////////////////////////////////////////////////////

// bandpass filter centred around 1000 Hz
// sampling rate = 8000 Hz

#define FILTER_LEN  63
double coeffs[ FILTER_LEN ] =
{
-0.0448093,  0.0322875,   0.0181163,   0.0087615,   0.0056797,
0.0086685,  0.0148049,   0.0187190,   0.0151019,   0.0027594,
-0.0132676, -0.0232561,  -0.0187804,   0.0006382,   0.0250536,
0.0387214,  0.0299817,   0.0002609,  -0.0345546,  -0.0525282,
-0.0395620,  0.0000246,   0.0440998,   0.0651867,   0.0479110,
0.0000135, -0.0508558,  -0.0736313,  -0.0529380,  -0.0000709,
0.0540186,  0.0766746,   0.0540186,  -0.0000709,  -0.0529380,
-0.0736313, -0.0508558,   0.0000135,   0.0479110,   0.0651867,
0.0440998,  0.0000246,  -0.0395620,  -0.0525282,  -0.0345546,
0.0002609,  0.0299817,   0.0387214,   0.0250536,   0.0006382,
-0.0187804, -0.0232561,  -0.0132676,   0.0027594,   0.0151019,
0.0187190,  0.0148049,   0.0086685,   0.0056797,   0.0087615,
0.0181163,  0.0322875,  -0.0448093
};

void intToFloat( int16_t *input, double *output, int length )
{
int i;

for ( i = 0; i < length; i++ ) {
output[i] = (double)input[i];
}
}

void floatToInt( double *input, int16_t *output, int length )
{
int i;

for ( i = 0; i  32767.0 ) {
input[i] = 32767.0;
} else if ( input[i] < -32768.0 ) {
input[i] = -32768.0;
}
// convert
output[i] = (int16_t)input[i];
}
}

// number of samples to read per loop
#define SAMPLES   80

int main( void )
{
int size;
int16_t input[SAMPLES];
int16_t output[SAMPLES];
double floatInput[SAMPLES];
double floatOutput[SAMPLES];
FILE   *in_fid;
FILE   *out_fid;

// open the input waveform file
in_fid = fopen( "input.pcm", "rb" );
if ( in_fid == 0 ) {
printf("couldn't open input.pcm");
return;
}

// open the output waveform file
out_fid = fopen( "outputFloat.pcm", "wb" );
if ( out_fid == 0 ) {
printf("couldn't open outputFloat.pcm");
return;
}

// initialize the filter
firFloatInit();

// process all of the samples
do {
size = fread( input, sizeof(int16_t), SAMPLES, in_fid );
// convert to doubles
intToFloat( input, floatInput, size );
// perform the filtering
firFloat( coeffs, floatInput, floatOutput, size,
FILTER_LEN );
// convert to ints
floatToInt( floatOutput, output, size );
// write samples to file
fwrite( output, sizeof(int16_t), size, out_fid );
} while ( size != 0 );

fclose( in_fid );
fclose( out_fid );

return 0;
}

First is the definition of the working array to hold the input samples. I have defined the array length to handle up to 80 input samples per function call, and a filter length up to 63 coefficients. In other words, the filter function that uses this working array will support between 1 to 80 input samples, and a filter length between 1 and 63.

Next is the initialization function, which simply zeroes the entire input sample array. This insures that all of the inputs that come before the first actual input are set to zero.

Then comes the FIR filtering function itself. The function takes pointers to the filter coefficients, the new input samples, and the output sample array. The “length” argument specifies the number of input samples to process (must be between 0 and 80 inclusive). The “filterLength” argument specifies the number of coefficients in the filter (must be between 1 and 63 inclusive). Note that the same filter coefficients and filterLength should be passed in each time the firFloat function is called, until the entire input is processed.

The first step in the function is the storage of the new samples. Note closely where the samples are placed in the array.

Next comes the outer loop that processes each input sample to produce one output sample. The outer loop sets up pointers to the coefficients and the input samples, and zeroes an accumulator value for the multiply-accumulate operation in the inner loop. It also stores each calculated output. The inner loop simply performs the multiply-accumulate operation.

Finally comes the step to move the input samples back in time. This is probably the most difficult step in the FIR filter and the part that always takes me the longest to work out. Pay close attention to the position of the samples to move, and the number of samples moved. The amount of time shift is equal to the number of input samples just processed. The number of samples to move is one less than the number of coefficients in the filter. Also note the use of the memmove function rather than the memcpy function, since there is a potential for moving overlapping data (whether or not the data overlaps depends on the relative lengths of the input samples and number of coefficients).

That concludes the code example for the FIR filter. In Part 2, I will show how to do the same thing using fixed point operations.

### Fixed Point Extensions to the C Programming Language

September 10, 2009

Recently I ran across an ISO specification for extensions to the C programming language to support fixed point types. The types are defined in a header file called stdfix.h. I have attached an early draft of the ISO spec (from 2006) here:

fixed_point_C_spec

I don’t think the extensions simplify the use of fixed types very much. The programmer still needs to know how many bits are allocated to integer and fractional parts, and how the number and positions of bits may change (during multiplication for example). What the extensions do provide is a way to access the saturation and rounding modes of the processor without writing assembly code. With this level of access, it is possible to write much more efficient C code to handle these operations.

The advantages of C code over assembly are quicker coding and debugging, and more portable code (that is, code that can run on more than one type of processor). However, I noticed that details such as fixed point fractional points and handling of rounding are implementation dependent. So the portability may only be applicable for “similar” processors.

I have never coded anything using the stdfix.h definitions. As far as I can see, the GCC compiler and the Dinkumware libraries are the only tools using these extensions. I’m not sure if or when it will come into popular use, but it’s something to consider if one is coding fixed point math operations in C.