From 71e8bd7532b63e4763b456820967ccabaed10a6a Mon Sep 17 00:00:00 2001 From: spdis Date: Fri, 19 Sep 2025 00:51:18 +0800 Subject: [PATCH] =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E4=B8=8A=E7=BA=BF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- audio_model_data.h | 937 ++++++++++++++++++++++++++++++++++++++++++++ audio_model_esp32.h | 355 +++++++++++++++++ 2 files changed, 1292 insertions(+) create mode 100644 audio_model_data.h create mode 100644 audio_model_esp32.h diff --git a/audio_model_data.h b/audio_model_data.h new file mode 100644 index 0000000..3797df3 --- /dev/null +++ b/audio_model_data.h @@ -0,0 +1,937 @@ +#ifndef AUDIO_MODEL_DATA_H +#define AUDIO_MODEL_DATA_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include + +// 模型数据大小 +#define AUDIO_MODEL_SIZE 10976 + +// 模型数据数组 +static const unsigned char audio_model_data[AUDIO_MODEL_SIZE] = { + + 0x20, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x00, 0x00, + 0x14, 0x00, 0x20, 0x00, 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0x10, 0x00, + 0x0f, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x09, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, + 0xc8, 0xff, 0xff, 0xff, 0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, + 0xd4, 0xff, 0xff, 0xff, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, + 0x04, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0xe8, 0xff, 0xff, 0xff, + 0x12, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0xf4, 0xff, 0xff, 0xff, + 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x0c, 0x00, 0x0c, 0x00, + 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04 +}; + +#ifdef __cplusplus +} +#endif + +#endif // AUDIO_MODEL_DATA_H diff --git a/audio_model_esp32.h b/audio_model_esp32.h new file mode 100644 index 0000000..4b3a962 --- /dev/null +++ b/audio_model_esp32.h @@ -0,0 +1,355 @@ +#ifndef AUDIO_MODEL_ESP32_H +#define AUDIO_MODEL_ESP32_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include +#include +#include +#include "audio_model_data.h" + +// ==================== 模型配置参数 ==================== +#define SAMPLE_RATE 16000 // 音频采样率 (Hz) +#define AUDIO_DURATION_MS 2000 // 音频片段时长 (毫秒) +#define N_MELS 32 // Mel频谱图频率bins数量 +#define N_FFT 1024 // FFT窗口大小 +#define HOP_LENGTH 512 // 跳跃长度 +#define NUM_CLASSES 4 // 分类数量 + +// 计算得出的参数 +#define AUDIO_BUFFER_SIZE (SAMPLE_RATE * AUDIO_DURATION_MS / 1000) // 32000 samples +#define MEL_FRAMES ((AUDIO_BUFFER_SIZE - N_FFT) / HOP_LENGTH + 1) // 约63帧 +#define INPUT_SIZE (MEL_FRAMES * N_MELS) // 输入特征大小 + +// 预处理参数 +#define MEL_FMIN 0.0f // Mel滤波器最低频率 +#define MEL_FMAX 8000.0f // Mel滤波器最高频率 +#define WINDOW_TYPE_HANN 1 // 汉宁窗 +#define ENERGY_THRESHOLD 0.01f // 音频活动检测阈值 +#define CONFIDENCE_THRESHOLD 0.6f // 预测置信度阈值 + +// ==================== 数据结构定义 ==================== + +// 音频分类枚举 +typedef enum { + AUDIO_CLASS_PERSON_PRESENT = 0, // 室内有人 + AUDIO_CLASS_DOOR_CLOSING = 1, // 关门 + AUDIO_CLASS_KEY_JINGLING = 2, // 钥匙弹子声 + AUDIO_CLASS_PERSON_ABSENT = 3 // 室内无人 +} AudioClassType; + +// 预测结果结构体 +typedef struct { + AudioClassType predicted_class; // 预测的类别 + float confidence; // 最高置信度 (0.0 - 1.0) + float class_probabilities[NUM_CLASSES]; // 各类别概率 + bool is_valid; // 预测结果是否有效 + uint32_t inference_time_us; // 推理耗时(微秒) +} AudioPredictionResult; + +// 音频预处理状态 +typedef struct { + float* mel_buffer; // Mel特征缓冲区 + float* fft_buffer; // FFT计算缓冲区 + float* window_buffer; // 窗函数缓冲区 + bool is_initialized; // 是否已初始化 +} AudioPreprocessor; + +// ==================== 核心API函数 ==================== + +/** + * @brief 初始化音频模型 + * @return 0: 成功, -1: 失败 + * @note 必须在使用其他函数前调用 + */ +int audio_model_init(void); + +/** + * @brief 清理音频模型资源 + * @note 程序结束时调用，释放内存 + */ +void audio_model_cleanup(void); + +/** + * @brief 音频预测函数（完整版） + * @param audio_data 输入音频数据指针 + * @param audio_length 音频数据长度（样本数） + * @param result 预测结果输出指针 + * @return 0: 成功, -1: 失败 + * + * 音频数据格式要求： + * - 数据类型: int16_t (16位有符号整数) + * - 采样率: 16000 Hz + * - 声道数: 单声道 + * - 数据长度: 32000 samples (2秒) + * - 数值范围: -32768 到 32767 + * - 字节序: 小端序(Little Endian) + * + * 示例调用： + * int16_t audio_buffer[AUDIO_BUFFER_SIZE]; + * AudioPredictionResult result; + * // ... 填充audio_buffer ... + * int ret = audio_model_predict(audio_buffer, AUDIO_BUFFER_SIZE, &result); + */ +int audio_model_predict(const int16_t* audio_data, int audio_length, AudioPredictionResult* result); + +/** + * @brief 音频预测函数（简化版） + * @param audio_data 输入音频数据指针 + * @param audio_length 音频数据长度 + * @param predicted_class 预测类别输出 + * @param confidence 置信度输出 + * @return 0: 成功, -1: 失败 + */ +int audio_model_predict_simple(const int16_t* audio_data, int audio_length, + AudioClassType* predicted_class, float* confidence); + +// ==================== 数据预处理函数 ==================== + +/** + * @brief 音频数据预处理（完整流程） + * @param audio_data 原始音频数据 (int16_t格式) + * @param audio_length 音频长度 + * @param mel_features 输出的Mel特征 (大小为INPUT_SIZE) + * @return 0: 成功, -1: 失败 + * + * 预处理步骤： + * 1. 数据类型转换 (int16_t -> float) + * 2. 归一化处理 ([-1.0, 1.0]) + * 3. 预加重滤波 + * 4. 加窗处理 (汉宁窗) + * 5. FFT变换 + * 6. 功率谱计算 + * 7. Mel滤波器组 + * 8. 对数变换 + * 9. 特征归一化 + */ +int preprocess_audio_to_mel(const int16_t* audio_data, int audio_length, float* mel_features); + +/** + * @brief 音频数据归一化 + * @param audio_data 输入音频数据 + * @param length 数据长度 + * @param normalized_data 归一化后的数据输出 + * @return 0: 成功, -1: 失败 + */ +int normalize_audio_data(const int16_t* audio_data, int length, float* normalized_data); + +/** + * @brief 计算Mel频谱图 + * @param audio_float 浮点音频数据 + * @param length 数据长度 + * @param mel_spectrogram 输出的Mel频谱图 + * @return 0: 成功, -1: 失败 + */ +int compute_mel_spectrogram(const float* audio_float, int length, float* mel_spectrogram); + +/** + * @brief 应用汉宁窗 + * @param data 输入数据 + * @param length 数据长度 + * @param windowed_data 加窗后的数据 + * @return 0: 成功, -1: 失败 + */ +int apply_hann_window(const float* data, int length, float* windowed_data); + +// ==================== 辅助函数 ==================== + +/** + * @brief 检测音频活动 + * @param audio_data 音频数据 + * @param length 数据长度 + * @param threshold 能量阈值 + * @return true: 检测到音频活动, false: 静音 + */ +bool detect_audio_activity(const int16_t* audio_data, int length, float threshold); + +/** + * @brief 计算音频RMS能量 + * @param audio_data 音频数据 + * @param length 数据长度 + * @return RMS能量值 + */ +float calculate_rms_energy(const int16_t* audio_data, int length); + +/** + * @brief 获取类别名称（英文） + * @param class_id 类别ID + * @return 类别名称字符串 + */ +const char* get_class_name_en(AudioClassType class_id); + +/** + * @brief 获取类别名称（中文） + * @param class_id 类别ID + * @return 类别名称字符串 + */ +const char* get_class_name_cn(AudioClassType class_id); + +/** + * @brief 验证音频数据格式 + * @param audio_data 音频数据指针 + * @param length 数据长度 + * @return true: 格式正确, false: 格式错误 + */ +bool validate_audio_format(const int16_t* audio_data, int length); + +/** + * @brief 打印预测结果 + * @param result 预测结果指针 + */ +void print_prediction_result(const AudioPredictionResult* result); + +// ==================== 性能监控函数 ==================== + +/** + * @brief 获取上次推理耗时 + * @return 推理时间(微秒) + */ +uint32_t get_last_inference_time_us(void); + +/** + * @brief 获取模型内存使用情况 + * @param model_memory 模型占用内存(字节) + * @param buffer_memory 缓冲区占用内存(字节) + * @return 0: 成功, -1: 失败 + */ +int get_memory_usage(size_t* model_memory, size_t* buffer_memory); + +/** + * @brief 获取预测统计信息 + * @param total_predictions 总预测次数 + * @param successful_predictions 成功预测次数 + * @param average_confidence 平均置信度 + * @return 0: 成功, -1: 失败 + */ +int get_prediction_statistics(uint32_t* total_predictions, uint32_t* successful_predictions, + float* average_confidence); + +// ==================== 配置函数 ==================== + +/** + * @brief 设置置信度阈值 + * @param threshold 新的阈值 (0.0 - 1.0) + * @return 0: 成功, -1: 失败 + */ +int set_confidence_threshold(float threshold); + +/** + * @brief 获取当前置信度阈值 + * @return 当前阈值 + */ +float get_confidence_threshold(void); + +/** + * @brief 启用/禁用调试模式 + * @param enable true: 启用, false: 禁用 + */ +void set_debug_mode(bool enable); + +/** + * @brief 检查调试模式状态 + * @return true: 已启用, false: 已禁用 + */ +bool is_debug_mode_enabled(void); + +// ==================== 错误处理 ==================== + +/** + * @brief 获取最后一次错误信息 + * @return 错误信息字符串 + */ +const char* get_last_error_message(void); + +/** + * @brief 清除错误状态 + */ +void clear_error_status(void); + +// ==================== 常量定义 ==================== + +// 类别名称数组（英文） +static const char* CLASS_NAMES_EN[NUM_CLASSES] = { + "person_present", // 室内有人 + "door_closing", // 关门 + "key_jingling", // 钥匙弹子声 + "person_absent" // 室内无人 +}; + +// 类别名称数组（中文） +static const char* CLASS_NAMES_CN[NUM_CLASSES] = { + "室内有人", + "关门声", + "钥匙声", + "室内无人" +}; + +// ==================== 模型数据访问函数 ==================== + +/** + * @brief 获取模型数据指针 + * @return 模型数据指针 + */ +const unsigned char* get_audio_model_data(void); + +/** + * @brief 获取模型数据大小 + * @return 模型数据大小（字节） + */ +size_t get_audio_model_size(void); + +// ==================== 使用示例 ==================== +/* +使用示例代码： + +#include "audio_model_esp32.h" + +void example_usage() { + // 1. 初始化模型 + if (audio_model_init() != 0) { + printf("模型初始化失败\n"); + return; + } + + // 2. 准备音频数据 + int16_t audio_buffer[AUDIO_BUFFER_SIZE]; + // ... 从麦克风或其他源获取音频数据 ... + + // 3. 验证音频格式 + if (!validate_audio_format(audio_buffer, AUDIO_BUFFER_SIZE)) { + printf("音频格式不正确\n"); + return; + } + + // 4. 进行预测 + AudioPredictionResult result; + if (audio_model_predict(audio_buffer, AUDIO_BUFFER_SIZE, &result) == 0) { + // 5. 处理预测结果 + if (result.is_valid && result.confidence > 0.6f) { + printf("预测类别: %s (置信度: %.2f)\n", + get_class_name_cn(result.predicted_class), + result.confidence); + } + } + + // 6. 清理资源 + audio_model_cleanup(); +} + +音频数据获取示例（ESP32 I2S）： +```c +#include "driver/i2s.h" + +void get_audio_data(int16_t* buffer, size_t buffer_size) { + size_t bytes_read; + i2s_read(I2S_NUM_0, buffer, buffer_size * sizeof(int16_t), &bytes_read, portMAX_DELAY); +} +``` +*/ + +#ifdef __cplusplus +} + +#endif // AUDIO_MODEL_ESP32_H \ No newline at end of file