gmm-ubm c++代码

GMM-UBM (Gaussian Mixture Model - Universal Background Model) 是一种语音识别中常用的声纹识别方法。下面是一个简化的 GMM-UBM 的 C 代码示例：

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

#define MAX_ITERATIONS 1000
#define MAX_COMPONENTS 16
#define FEATURE_DIMENSION 13

typedef struct {
    double mean[FEATURE_DIMENSION];
    double covariance[FEATURE_DIMENSION][FEATURE_DIMENSION];
    double weight;
} Gaussian;

typedef struct {
    int num_components;
    Gaussian components[MAX_COMPONENTS];
} GMM;

void train_gmm_ubm(double features[][FEATURE_DIMENSION], int num_features, GMM *gmm) {
    int i, j, k, t;
    int num_iterations = 0;
    double log_likelihood = 0.0;
    double prev_log_likelihood = -INFINITY;
    double responsibilities[num_features][MAX_COMPONENTS];
    
    // Initialize GMM parameters randomly
    for (i = 0; i < gmm->num_components; i++) {
        for (j = 0; j < FEATURE_DIMENSION; j++) {
            gmm->components[i].mean[j] = (rand() / (double)RAND_MAX) * 10.0;
        }
        for (j = 0; j < FEATURE_DIMENSION; j++) {
            for (k = 0; k < FEATURE_DIMENSION; k++) {
                gmm->components[i].covariance[j][k] = (rand() / (double)RAND_MAX) * 10.0;
            }
        }
        gmm->components[i].weight = 1.0 / gmm->num_components;
    }
    
    while (num_iterations < MAX_ITERATIONS && log_likelihood - prev_log_likelihood > 0.01) {
        prev_log_likelihood = log_likelihood;
        log_likelihood = 0.0;
        
        // Expectation step: calculate responsibilities
        for (t = 0; t < num_features; t++) {
            double sum = 0.0;
            for (i = 0; i < gmm->num_components; i++) {
                double exponent = 0.0;
                double determinant = 1.0;
                
                // Calculate Mahalanobis distance
                for (j = 0; j < FEATURE_DIMENSION; j++) {
                    for (k = 0; k < FEATURE_DIMENSION; k++) {
                        determinant *= gmm->components[i].covariance[j][k];
                    }
                    exponent += (features[t][j] - gmm->components[i].mean[j]) *
                                (features[t][j] - gmm->components[i].mean[j]) /
                                gmm->components[i].covariance[j][j];
                }
                
                responsibilities[t][i] = gmm->components[i].weight * exp(-0.5 * exponent) /
                                         sqrt(pow(2 * M_PI, FEATURE_DIMENSION) * determinant);
                sum += responsibilities[t][i];
            }
            
            // Normalize responsibilities
            for (i = 0; i < gmm->num_components; i++) {
                responsibilities[t][i] /= sum;
            }
            
            log_likelihood += log(sum);
        }
        
        // Maximization step: update GMM parameters
        for (i = 0; i < gmm->num_components; i++) {
            double total_weight = 0.0;
            
            // Update mean
            for (j = 0; j < FEATURE_DIMENSION; j++) {
                double weighted_sum = 0.0;
                for (t = 0; t < num_features; t++) {
                    weighted_sum += responsibilities[t][i] * features[t][j];
                }
                gmm->components[i].mean[j] = weighted_sum / sum;
            }
            
            // Update covariance
            for (j = 0; j < FEATURE_DIMENSION; j++) {
                for (k = 0; k < FEATURE_DIMENSION; k++) {
                    double weighted_sum = 0.0;
                    for (t = 0; t < num_features; t++) {
                        weighted_sum += responsibilities[t][i] *
                                        (features[t][j] - gmm->components[i].mean[j]) *
                                        (features[t][k] - gmm->components[i].mean[k]);
                    }
                    gmm->components[i].covariance[j][k] = weighted_sum / sum;
                }
            }
            
            // Update weight
            for (t = 0; t < num_features; t++) {
                total_weight += responsibilities[t][i];
            }
            gmm->components[i].weight = total_weight / num_features;
        }
        
        num_iterations++;
    }
}

int main() {
    // Example usage of GMM-UBM training
    double features[100][FEATURE_DIMENSION];
    int num_features = 100;
    GMM gmm;
    
    // Load features from dataset
    
    // Train GMM-UBM model
    gmm.num_components = 4;
    train_gmm_ubm(features, num_features, &gmm);
    
    return 0;
}

上述代码是一个简单的 GMM-UBM 训练的示例，其中包含了期望最大化 (EM) 算法的实现。你可以将训练数据加载到 `features` 数组中，并按需设置 GMM 组件的数量和特征维度。代码中的注释提供了更详细的解释。请注意，这只是一个简化版本的代码，实际应用中可能需要进行更多的参数调整和优化。