annotate src/vector.c @ 140:67f6b6e63d45

added Ooura implementation to repository

/* libxtract feature extraction library
 *
 * Copyright (C) 2006 Jamie Bullock
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301,
 * USA.
 */


/* xtract_vector.c: defines functions that extract a feature as a single value from an input vector */

#include <math.h>
#include <string.h>
#include <stdlib.h>

#include "fftsg.h"

#include "xtract/libxtract.h"
#include "xtract_macros_private.h"

#ifndef roundf
float roundf(float f)
{
    if (f - (int)f >= 0.5)
        return (float)((int)f + 1);
    else
        return (float)((int)f);
}
#endif

#ifndef powf
#define powf pow
#endif

#ifndef expf
#define expf exp
#endif

#ifndef sqrtf
#define sqrtf sqrt
#endif

#ifndef fabsf
#define fabsf fabs
#endif

#include "xtract_globals_private.h"
#include "xtract_macros_private.h"

int xtract_spectrum(const float *data, const int N, const void *argv, float *result)
{

    int vector     = 0;
    int withDC     = 0;
    int normalise  = 0;
    float q        = 0.f;
    float temp     = 0.f;
    float max      = 0.f;
    float NxN      = XTRACT_SQ(N);
    float *marker  = NULL;
    size_t bytes   = N * sizeof(double);
    double *rfft    = NULL;
    unsigned int n = 0;
    unsigned int m = 0;
    unsigned int nx2 = 0;
    unsigned int M = N >> 1;

    rfft = (double *)malloc(bytes);
    //memcpy(rfft, data, bytes);

	for(n = 0; n < N; ++n){
		rfft[n] = (double)data[n];
	}

    q = *(float *)argv;
    vector = (int)*((float *)argv+1);
    withDC = (int)*((float *)argv+2);
    normalise = (int)*((float *)argv+3);

    XTRACT_CHECK_q;

    if(!ooura_data_spectrum.initialised)
    {
        fprintf(stderr,
                "libxtract: error: xtract_spectrum() failed, "
                "fft data unitialised.\n");
        return XTRACT_NO_RESULT;
    }

    /* ooura is in-place
     * the output format seems to be
     * a[0] - DC, a[1] - nyquist, a[2...N-1] - remaining bins
     */
    rdft(N, 1, rfft, ooura_data_spectrum.ooura_ip, 
            ooura_data_spectrum.ooura_w);

    switch(vector)
    {

        case XTRACT_LOG_MAGNITUDE_SPECTRUM:
        for(n = 0, m = 0; m < M; ++n, ++m)
        {
            if(!withDC && n == 0)
            {
                continue;
            }
            nx2  = n * 2;
            temp = XTRACT_SQ(rfft[nx2]) + XTRACT_SQ(rfft[nx2+1]);
            if (temp > XTRACT_LOG_LIMIT)
            {
                temp = logf(sqrtf(temp) / (float)N);
            }
            else
            {
                temp = XTRACT_LOG_LIMIT_DB;
            }
            result[m] =
                /* Scaling */
                (temp + XTRACT_DB_SCALE_OFFSET) /
                XTRACT_DB_SCALE_OFFSET;

            XTRACT_SET_FREQUENCY;
            XTRACT_GET_MAX;
        }
        break;

    case XTRACT_POWER_SPECTRUM:
        for(n = 0, m = 0; m < M; ++n, ++m)
        {
            if(!withDC && n == 0)
            {
                ++n;
            }
            result[m] = (XTRACT_SQ(rfft[n]) + XTRACT_SQ(rfft[N - n])) / NxN;
            XTRACT_SET_FREQUENCY;
            XTRACT_GET_MAX;
        }
        break;

    case XTRACT_LOG_POWER_SPECTRUM:
        for(n = 0, m = 0; m < M; ++n, ++m)
        {
            if(!withDC && n == 0)
            {
                ++n;
            }
            if ((temp = XTRACT_SQ(rfft[n]) + XTRACT_SQ(rfft[N - n])) >
                    XTRACT_LOG_LIMIT)
                temp = logf(temp / NxN);
            else
                temp = XTRACT_LOG_LIMIT_DB;

            result[m] = (temp + XTRACT_DB_SCALE_OFFSET) /
                        XTRACT_DB_SCALE_OFFSET;
            XTRACT_SET_FREQUENCY;
            XTRACT_GET_MAX;
        }
        break;

    default:
        /* MAGNITUDE_SPECTRUM */
        for(n = 0, m = 0; m < M; ++n, ++m)
        {
            marker = &result[m];

            if(n==0 && !withDC) /* discard DC and keep Nyquist */
            {
                ++n;
                marker = &result[M-1];
            }
            if(n==1 && withDC) /* discard Nyquist */
            {
                ++n;
            }

            *marker = (float)(sqrt(XTRACT_SQ(rfft[n*2]) +
                        XTRACT_SQ(rfft[n*2+1])) / (double)N);

            XTRACT_SET_FREQUENCY;
            XTRACT_GET_MAX;

        }
        break;
    }

    if(normalise)
    {
        for(n = 0; n < M; n++)
            result[n] /= max;
    }

    free(rfft);

    return XTRACT_SUCCESS;
}

int xtract_autocorrelation_fft(const float *data, const int N, const void *argv, float *result)
{

    double *rfft = NULL;
    int n        = 0;
    int M        = 0;

    M = N << 1;

    /* Zero pad the input vector */
    rfft = (double *)calloc(M, sizeof(double));
    memcpy(rfft, data, N * sizeof(float));

    rdft(M, 1, rfft, ooura_data_autocorrelation_fft.ooura_ip, 
            ooura_data_autocorrelation_fft.ooura_w);

    for(n = 2; n < M; n++)
    {
        rfft[n*2] = XTRACT_SQ(rfft[n*2]) + XTRACT_SQ(rfft[n*2+1]);
        rfft[n*2+1] = 0.f;
    }

    rfft[0] = XTRACT_SQ(rfft[0]);
    rfft[1] = XTRACT_SQ(rfft[1]);

    rdft(M, -1, rfft, ooura_data_autocorrelation_fft.ooura_ip,
            ooura_data_autocorrelation_fft.ooura_w);

    /* Normalisation factor */
    M = M * N;

    for(n = 0; n < N; n++)
        result[n] = rfft[n] / (float)M;

    free(rfft);

    return XTRACT_SUCCESS;
}

int xtract_mfcc(const float *data, const int N, const void *argv, float *result)
{

    xtract_mel_filter *f;
    int n, filter;

    f = (xtract_mel_filter *)argv;

    for(filter = 0; filter < f->n_filters; filter++)
    {
        result[filter] = 0.f;
        for(n = 0; n < N; n++)
        {
            result[filter] += data[n] * f->filters[filter][n];
        }
        result[filter] = logf(result[filter] < XTRACT_LOG_LIMIT ? XTRACT_LOG_LIMIT : result[filter]);
    }

    xtract_dct(result, f->n_filters, NULL, result);

    return XTRACT_SUCCESS;
}

int xtract_dct(const float *data, const int N, const void *argv, float *result)
{

	int n;
        int m;
        float *temp = calloc(N, sizeof(float));

        for (n = 0; n < N; ++n)
        {
            for(m = 1; m <= N; ++m) {
                temp[n] += data[m - 1] * cos(M_PI * (n / (float)N) * (m - 0.5));
            }
        }

        return XTRACT_SUCCESS;
}

int xtract_autocorrelation(const float *data, const int N, const void *argv, float *result)
{

    /* Naive time domain implementation  */

    int n = N, i;

    float corr;

    while(n--)
    {
        corr = 0;
        for(i = 0; i < N - n; i++)
        {
            corr += data[i] * data[i + n];
        }
        result[n] = corr / N;
    }

    return XTRACT_SUCCESS;
}

int xtract_amdf(const float *data, const int N, const void *argv, float *result)
{

    int n = N, i;

    float md, temp;

    while(n--)
    {
        md = 0.f;
        for(i = 0; i < N - n; i++)
        {
            temp = data[i] - data[i + n];
            temp = (temp < 0 ? -temp : temp);
            md += temp;
        }
        result[n] = md / (float)N;
    }

    return XTRACT_SUCCESS;
}

int xtract_asdf(const float *data, const int N, const void *argv, float *result)
{

    int n = N, i;

    float sd;

    while(n--)
    {
        sd = 0.f;
        for(i = 0; i < N - n; i++)
        {
            /*sd = 1;*/
            sd += XTRACT_SQ(data[i] - data[i + n]);
        }
        result[n] = sd / (float)N;
    }

    return XTRACT_SUCCESS;
}

int xtract_bark_coefficients(const float *data, const int N, const void *argv, float *result)
{

    int *limits, band, n;

    limits = (int *)argv;

    for(band = 0; band < XTRACT_BARK_BANDS - 1; band++)
    {
        result[band] = 0.f;
        for(n = limits[band]; n < limits[band + 1]; n++)
            result[band] += data[n];
    }

    return XTRACT_SUCCESS;
}

int xtract_peak_spectrum(const float *data, const int N, const void *argv, float *result)
{

    float threshold, max, y, y2, y3, p, q, *input = NULL;
    size_t bytes;
    int n = N, rv = XTRACT_SUCCESS;

    threshold = max = y = y2 = y3 = p = q = 0.f;

    if(argv != NULL)
    {
        q = ((float *)argv)[0];
        threshold = ((float *)argv)[1];
    }
    else
        rv = XTRACT_BAD_ARGV;

    if(threshold < 0 || threshold > 100)
    {
        threshold = 0;
        rv = XTRACT_BAD_ARGV;
    }

    XTRACT_CHECK_q;

    input = (float *)calloc(N,  sizeof(float));

    bytes = N * sizeof(float);

    if(input != NULL)
        input = memcpy(input, data, bytes);
    else
        return XTRACT_MALLOC_FAILED;

    while(n--)
        max = XTRACT_MAX(max, input[n]);

    threshold *= .01 * max;

    result[0] = 0;
    result[N] = 0;

    for(n = 1; n < N; n++)
    {
        if(input[n] >= threshold)
        {
            if(input[n] > input[n - 1] && n + 1 < N && input[n] > input[n + 1])
            {
                result[N + n] = q * (n + (p = .5 * ((y = input[n-1]) -
                                                    (y3 = input[n+1])) / (input[n - 1] - 2 *
                                                            (y2 = input[n]) + input[n + 1])));
                result[n] = y2 - .25 * (y - y3) * p;
            }
            else
            {
                result[n] = 0;
                result[N + n] = 0;
            }
        }
        else
        {
            result[n] = 0;
            result[N + n] = 0;
        }
    }

    free(input);
    return (rv ? rv : XTRACT_SUCCESS);
}

int xtract_harmonic_spectrum(const float *data, const int N, const void *argv, float *result)
{

    int n = (N >> 1), M = n;

    const float *freqs, *amps;
    float f0, threshold, ratio, nearest, distance;

    amps = data;
    freqs = data + n;
    f0 = *((float *)argv);
    threshold = *((float *)argv+1);

    ratio = nearest = distance = 0.f;

    while(n--)
    {
        if(freqs[n])
        {
            ratio = freqs[n] / f0;
            nearest = roundf(ratio);
            distance = fabs(nearest - ratio);
            if(distance > threshold)
                result[n] = result[M + n] = 0.f;
            else
            {
                result[n] = amps[n];
                result[M + n] = freqs[n];
            }
        }
        else
            result[n] = result[M + n] = 0.f;
    }
    return XTRACT_SUCCESS;
}

int xtract_lpc(const float *data, const int N, const void *argv, float *result)
{

    int i, j, k, M, L;
    float r = 0.f,
          error = 0.f;

    float *ref = NULL,
           *lpc = NULL ;

    error = data[0];
    k = N; /* The length of *data */
    L = N - 1; /* The number of LPC coefficients */
    M = L * 2; /* The length of *result */
    ref = result;
    lpc = result+L;

    if(error == 0.0)
    {
        memset(result, 0, M * sizeof(float));
        return XTRACT_NO_RESULT;
    }

    memset(result, 0, M * sizeof(float));

    for (i = 0; i < L; i++)
    {

        /* Sum up this iteration's reflection coefficient. */
        r = -data[i + 1];
        for (j = 0; j < i; j++)
            r -= lpc[j] * data[i - j];
        ref[i] = r /= error;

        /* Update LPC coefficients and total error. */
        lpc[i] = r;
        for (j = 0; j < i / 2; j++)
        {
            float tmp      = lpc[j];
            lpc[j]          = r * lpc[i - 1 - j];
            lpc[i - 1 - j] += r * tmp;
        }
        if (i % 2) lpc[j] += lpc[j] * r;

        error *= 1 - r * r;
    }

    return XTRACT_SUCCESS;
}

int xtract_lpcc(const float *data, const int N, const void *argv, float *result)
{

    /* Given N lpc coefficients extract an LPC cepstrum of size argv[0] */
    /* Based on an an algorithm by rabiner and Juang */

    int n, k;
    float sum;
    int order = N - 1; /* Eventually change this to Q = 3/2 p as suggested in Rabiner */
    int cep_length;

    if(argv == NULL)
        cep_length = N - 1; /* FIX: if we're going to have default values, they should come from the descriptor */
    else
        cep_length = *(int *)argv;
    //cep_length = (int)((float *)argv)[0];

    memset(result, 0, cep_length * sizeof(float));

    for (n = 1; n <= order && n <= cep_length; n++)
    {
        sum = 0.f;
        for (k = 1; k < n; k++)
            sum += k * result[k-1] * data[n - k];
        result[n-1] = data[n] + sum / n;
    }

    /* be wary of these interpolated values */
    for(n = order + 1; n <= cep_length; n++)
    {
        sum = 0.f;
        for (k = n - (order - 1); k < n; k++)
            sum += k * result[k-1] * data[n - k];
        result[n-1] = sum / n;
    }

    return XTRACT_SUCCESS;

}
//int xtract_lpcc_s(const float *data, const int N, const void *argv, float *result){
//    return XTRACT_SUCCESS;
//}

int xtract_subbands(const float *data, const int N, const void *argv, float *result)
{

    int n, bw, xtract_func, nbands, scale, start, lower, *argi, rv;

    argi = (int *)argv;

    xtract_func = argi[0];
    nbands = argi[1];
    scale = argi[2];
    start = argi[3];

    if(scale == XTRACT_LINEAR_SUBBANDS)
        bw = floorf((N - start) / nbands);
    else
        bw = start;

    lower = start;
    rv = XTRACT_SUCCESS;

    for(n = 0; n < nbands; n++)
    {

        /* Bounds sanity check */
        if(lower >= N || lower + bw >= N)
        {
            //   printf("n: %d\n", n);
            result[n] = 0.f;
            continue;
        }

        rv = xtract[xtract_func](data+lower, bw, NULL, &result[n]);

        if(rv != XTRACT_SUCCESS)
            return rv;

        switch(scale)
        {
        case XTRACT_OCTAVE_SUBBANDS:
            lower += bw;
            bw = lower;
            break;
        case XTRACT_LINEAR_SUBBANDS:
            lower += bw;
            break;
        }

    }

    return rv;

}