音频pcm数据相似度分析的c源码
时间: 2023-11-18 21:03:03 浏览: 36
以下是一个简单的音频PCM数据相似度分析的C语言源码示例:
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define SAMPLE_RATE 44100 // 采样率
#define NUM_SAMPLES 44100 // 采样数
#define WINDOW_SIZE 512 // 窗口大小
#define HOP_SIZE 256 // 跳跃大小
typedef struct {
float* data;
int size;
} AudioBuffer;
float calculate_rms(AudioBuffer* buffer) {
float sum = 0.0f;
for (int i = 0; i < buffer->size; i++) {
sum += buffer->data[i] * buffer->data[i];
}
return sqrtf(sum / buffer->size);
}
float calculate_similarity(AudioBuffer* buffer1, AudioBuffer* buffer2) {
int num_windows = (NUM_SAMPLES - WINDOW_SIZE) / HOP_SIZE + 1;
float* window1 = (float*)malloc(sizeof(float) * WINDOW_SIZE);
float* window2 = (float*)malloc(sizeof(float) * WINDOW_SIZE);
float* buffer1_rms = (float*)malloc(sizeof(float) * num_windows);
float* buffer2_rms = (float*)malloc(sizeof(float) * num_windows);
for (int i = 0; i < num_windows; i++) {
// 提取窗口
for (int j = 0; j < WINDOW_SIZE; j++) {
window1[j] = buffer1->data[i * HOP_SIZE + j];
window2[j] = buffer2->data[i * HOP_SIZE + j];
}
// 计算两个窗口的RMS
buffer1_rms[i] = calculate_rms(&(AudioBuffer){ window1, WINDOW_SIZE });
buffer2_rms[i] = calculate_rms(&(AudioBuffer){ window2, WINDOW_SIZE });
}
// 计算相似度
float sum = 0.0f;
for (int i = 0; i < num_windows; i++) {
sum += buffer1_rms[i] * buffer2_rms[i];
}
float similarity = sum / num_windows;
free(window1);
free(window2);
free(buffer1_rms);
free(buffer2_rms);
return similarity;
}
int main() {
AudioBuffer buffer1 = {
.data = (float*)malloc(sizeof(float) * NUM_SAMPLES),
.size = NUM_SAMPLES
};
AudioBuffer buffer2 = {
.data = (float*)malloc(sizeof(float) * NUM_SAMPLES),
.size = NUM_SAMPLES
};
// 填充音频数据
for (int i = 0; i < NUM_SAMPLES; i++) {
buffer1.data[i] = sinf(2.0f * M_PI * 440.0f * i / SAMPLE_RATE);
buffer2.data[i] = sinf(2.0f * M_PI * 440.0f * i / SAMPLE_RATE + M_PI / 2.0f);
}
float similarity = calculate_similarity(&buffer1, &buffer2);
printf("Similarity: %f\n", similarity);
free(buffer1.data);
free(buffer2.data);
return 0;
}
```
该示例代码使用RMS(均方根)和窗口技术来计算两个音频PCM数据的相似度。通过将音频数据分成多个窗口,然后计算每个窗口的RMS,最后计算RMS的平均值来计算相似度。这种方法可以减少时间域中的干扰,并更好地捕获频域中的相似度。该示例仅用于演示目的,实际应用中可能需要更复杂的算法来获得更准确的结果。