输入信号 → STFT → [WPE 去晚期混响] → [DNN 掩码估计] → 后处理平滑 → iSTFT → 输出
                    ↑                    ↑
              自适应参数更新         在线参数自适应

graph LR
    A[FP32 模型 1.2M] --> B[PTQ 量化 INT8]
    B --> C[逐层校准 KL 散度]
    C --> D[混合精度回退 敏感层 FP16]
    D --> E[算子融合 Conv+BN+Act]
    E --> F[图优化 常量折叠/死代码消除]
    F --> G[导出 NCNN/MNN/TFLite]
    G --> H[NPU 离线编译]

// 伪代码：任务图拓扑调度
TaskGraph g;
g.add("STFT", CPU_HIGH);                    // NEON 优化
g.add("WPE_RLS", CPU_HIGH);                 // 矩阵运算，单线程
g.add("DNN_Infer", NPU);                    // 异步提交
g.add("PostProcess", CPU_LOW);              // 平滑、增益补偿
g.add("iSTFT", CPU_HIGH);

g.set_dependency("STFT" -> "WPE_RLS");
g.set_dependency("WPE_RLS" -> "DNN_Infer");
g.set_dependency("DNN_Infer" -> "PostProcess");
g.set_dependency("PostProcess" -> "iSTFT");

// 双流水线：帧 n 与 n+1 并行
while (running) {
    g.execute_async(frame_id % 2);
    g.wait(frame_id % 2);
    frame_id++;
}

// 伪代码：每通道独立 RLS + 周期性协方差平均
struct MC_WPE_State {
    cplx_mat_t G[M];        // 各通道预测滤波器 [M, ML]
    cplx_mat_t P[M];        // 各通道逆相关矩阵 [ML, ML]
    cplx_vec_t X_buf[M];    // 延迟缓冲 [ML]
};

void mc_wpe_step(MC_WPE_State* st, cplx_vec_t X_in[M]) {
    // 1. 各通道并行 RLS 更新 (NEON/SIMD 向量化)
    #pragma omp parallel for num_threads(M)
    for (int m = 0; m < M; m++) {
        rls_update(&st->G[m], &st->P[m], &st->X_buf[m], X_in[m]);
    }
    // 2. 每 16 帧做一次协方差矩阵几何平均 (几何平均保证正定性)
    if (frame_cnt % 16 == 0) {
        cplx_mat_t P_avg = geometric_mean(st->P, M);  // P_avg = exp(mean(log(P_m)))
        for (int m = 0; m < M; m++) st->P[m] = P_avg; // 广播回各通道
    }
    // 3. 输出增强信号
    for (int m = 0; m < M; m++) Y_out[m] = X_in[m] - mat_vec_mul(st->G[m], st->X_buf[m]);
}

多通道 STFT → [空间协方差估计网络 Φ_ss, Φ_nn] → [MVDR Layer] → 单通道增强谱
                                    ↓
                              [WPE-RLS Layer] → 残余去混响
                                    ↓
                              [Complex TF-GridNet] → 精细掩码
                                    ↓
                              [后处理] → iSTFT

class MVDRLayer(nn.Module):
    def forward(self, X, phi_ss, phi_nn, ref_ch=0):
        # X: [B, M, F, T], phi_ss/nn: [B, M, M, F]
        # 正则化防止奇异
        phi_nn_reg = phi_nn + 1e-3 * torch.eye(M, device=X.device).view(1, M, M, 1)
        # 广义特征值分解求主特征向量 (Power Iteration 3 步近似)
        w = torch.linalg.solve(phi_nn_reg, phi_ss)  # [B, M, M, F]
        w = w[..., ref_ch, :]                       # 取参考通道行
        w = w / (w.conj().transpose(-2, -1) @ phi_nn_reg @ w).clamp_min(1e-8).sqrt()
        # 波束成形输出
        Y = (w.conj().unsqueeze(-1) * X).sum(dim=1) # [B, F, T]
        return Y, w

远端参考信号 x_far → [共享编码器] → [AEC 解码器] → 回声估计 ê
近端麦克风信号 x_near → [共享编码器] → [去混响解码器] → 纯净语音 s
                                    ↓
                              [DTD/双讲判决头] → 软掩码 λ_DTD

// 核心数据结构：带时间戳的环形缓冲
#define MAX_DELAY_FRAMES 8   // 支持最大 5 帧 Δ + 3 帧平滑
typedef struct {
    cplx_vec_t buf[MAX_DELAY_FRAMES][MAX_CH][FREQ_BINS];
    uint64_t   ts[MAX_DELAY_FRAMES];      // 单调递增时间戳 (us)
    int        head;                      // 写指针
    int        tail;                      // 读指针 (head - Δ)
} AlignedRingBuffer;

// 写入：STFT 后立即入队
void ring_push(AlignedRingBuffer* rb, cplx_vec_t* frame, uint64_t timestamp) {
    rb->ts[rb->head] = timestamp;
    memcpy(rb->buf[rb->head], frame, sizeof(cplx_vec_t)*MAX_CH*FREQ_BINS);
    rb->head = (rb->head + 1) % MAX_DELAY_FRAMES;
}

// 读取：WPE 需要 t-Δ 帧，精确对齐时间戳
cplx_vec_t* ring_peek_delayed(AlignedRingBuffer* rb, int delay_frames, uint64_t* out_ts) {
    int idx = (rb->head - delay_frames + MAX_DELAY_FRAMES) % MAX_DELAY_FRAMES;
    *out_ts = rb->ts[idx];
    return rb->buf[idx];
}

// 主循环：强制对齐
while (running) {
    uint64_t t_now = get_mono_us();
    // 1. 采集 + STFT
    cplx_vec_t X = stft_process(capture_frame());
    ring_push(&rb, &X, t_now);
    // 2. 取延迟帧给 WPE
    uint64_t t_wpe_in;
    cplx_vec_t* X_delayed = ring_peek_delayed(&rb, DELTA_FRAMES, &t_wpe_in);
    // 3. 校验时间戳单调性 (防驱动抖动)
    assert(t_now - t_wpe_in == DELTA_FRAMES * FRAME_SHIFT_US +- 500); // 容差 0.5 ms
    // 4. WPE + DNN + iSTFT ...
}

# 离线分析脚本：计算每层权重对 Loss 的二阶敏感度
def hessian_trace_sensitivity(model, calib_loader, device):
    model.eval()
    sensitivity = {}
    for name, param in model.named_parameters():
        if 'weight' not in name: continue
        grad_acc = torch.zeros_like(param)
        for x, _ in calib_loader:
            x = x.to(device)
            loss = model(x).sum()
            g = torch.autograd.grad(loss, param, create_graph=False)[0]
            grad_acc += g * g  # 对角近似 Fisher 信息
        sensitivity[name] = (grad_acc / len(calib_loader)).mean().item()
    return sensitivity

# 典型结果 (Complex TF-GridNet-Tiny):
# 最敏感: encoder.conv1 (0.87), decoder.conv_last (0.72), mvdr_layer (0.65)
# 最不敏感: 中间 GridNet blocks (0.03~0.12)

# CI/CD 集成：夜ly 回归测试
quant_regression:
  stages:
    - ptq_int8: {calib_size: 1024, metric: "PESQ>2.85, STOI>0.86"}
    - qat_int8: {epochs: 5, lr: 1e-4, trigger: "ptq_fail"}
    - gptq_int4: {group_size: 32, damp: 0.01, metric: "PESQ>2.78"}
    - mixed_precision: {policy: "sensitivity_top20_int8_rest_int4"}
  artifacts:
    - onnx_model
    - quantization_params.json  # 每层 scale/zero_point
    - accuracy_report.html
  alerts:
    - on: "metric_drop > 0.05 PESQ"
      action: "block_release + notify_algorithm_team"

云端 (Parameter Server)
    │
    ├── 全局主干模型 (Shared Backbone: Encoder + GridNet Blocks)  ← FedAvg 聚合
    │
    ├── 个性化适配器库 (LoRA Adapters per Room Type)             ← FedPer / pFedMe
    │     ├── MeetingRoom_Small (r=4)
    │     ├── MeetingRoom_Large (r=8)
    │     ├── GlassWall_Room (r=4)
    │     └── OpenPlan_Office (r=6)
    │
    └── 安全聚合协议 (SecAgg + DP-SGD)
          ├── 客户端本地训练 (1~2 epochs, batch=8)
          ├── 本地裁剪 (Clip Norm=1.0) + 高斯噪声 (σ=0.5)
          ├── 加密上传 (Paillier / CKKS)
          └── 服务端解密聚合 → 更新全局主干

# 端侧本地训练循环 (PyTorch Mobile / ExecuTorch)
def local_train_step(model, local_data, global_weights, adapter_id):
    # 1. 加载全局主干 + 个人适配器
    model.load_state_dict(global_weights, strict=False)
    adapter = load_lora_adapter(adapter_id)  # < 50 KB
    merge_lora(model, adapter, alpha=1.0)
    
    # 2. 冻结主干，仅训练适配器 + 最后一层 (参数量 < 0.5%)
    freeze_backbone(model)
    unfreeze_adapter_and_head(model)
    
    # 3. 隐私增强损失
    for epoch in range(LOCAL_EPOCHS):
        for x, target in local_data:
            # 目标：干净语音 (本地合成或用户确认片段)
            loss = si_snr_loss(model(x), target)
            # 差分隐私：梯度裁剪 + 噪声
            clip_grad_norm_(model.parameters(), MAX_NORM)
            add_gaussian_noise_(model.parameters(), sigma=DP_SIGMA)
            optimizer.step()
    
    # 4. 提取适配器增量上传
    delta_adapter = extract_lora_delta(model, adapter)
    encrypt_and_upload(delta_adapter)

{
  "model_version": "v2.3.1-rc4",
  "git_commit": "a1b2c3d4",
  "training_config_hash": "sha256:f4e5...",
  "federated_round": 142,
  "participating_clients": 12450,
  "dp_epsilon": 2.1,
  "dp_delta": 1e-5,
  "accuracy_metrics": {
    "pesq_wb": 2.89,
    "stoi": 0.87,
    "rtf_p99": 0.28
  },
  "compliance": {
    "gdpr_art25": "privacy_by_design",
    "pip_art28": "local_processing_only",
    "encryption": "AES-256-GCM + RSA-3072"
  },
  "signature": "ECDSA_P256:MEUCIQD..."
}

if (p_non_speech > 0.85) {
    // 1. WPE: 冻结预测系数 G, 仅做滤波
    wpe_set_mode(WPE_MODE_FREEZE);
    // 2. DNN: 掩码强制倾向保留 (避免误抑制瞬态语音起止)
    dnn_set_mask_bias(+3.0 dB); 
    // 3. 后处理: 谱减法兜底 (仅针对高频 > 4kHz)
    spectral_subtraction_highband(alpha=1.5);
}

# 1. 硬件计数器采样 (Linux perf / Android simpleperf)
perf record -g -e cycles,instructions,cache-misses,branch-misses -p <audio_pid> -- sleep 30
perf report --stdio > perf_report.txt

# 2. NPU 专用分析 (Qualcomm SNPE / MediaTek Neuron / RKNN)
snpe-profiler -m model.dlc -i input.raw -o profile.json --iterations 1000

# 3. 内存分配器追踪 (jemalloc / malloc_info)
MALLOC_CONF="prof:true,prof_prefix:jeprof" ./audio_server
jeprof --pdf ./audio_server jeprof.*.heap > heap.pdf

search_space = {
    "wpe_L": hp.choice([5, 8, 10, 12]),
    "wpe_lambda": hp.uniform(0.99, 0.999),
    "dnn_mask_smooth_beta": hp.uniform(0.5, 0.95),
    "postfilter_over_sub": hp.uniform(0.8, 1.2),
    "vad_threshold": hp.uniform(-40, -20),  # dB
}

// audio_frontend_types.h
#ifndef AUDIO_FRONTEND_TYPES_H
#define AUDIO_FRONTEND_TYPES_H

#include <stdint.h>
#include <complex.h>

#define MAX_CH 8
#define MAX_FREQ_BINS 257
#define MAX_WPE_ORDER 16
#define MAX_DELTA_FRAMES 8

typedef float complex cplx32_t;
typedef int16_t complex_i16_t __attribute__((ext_vector_type(2))); // INT8 复数打包

// WPE 状态 (每频段独立)
typedef struct {
    cplx32_t G[MAX_CH][MAX_CH * MAX_WPE_ORDER]; // 预测矩阵
    cplx32_t P[MAX_CH * MAX_WPE_ORDER][MAX_CH * MAX_WPE_ORDER]; // 逆相关矩阵
    cplx32_t X_buf[MAX_CH][MAX_WPE_ORDER];      // 延迟缓冲
    float    lambda;                            // 遗忘因子
    int      L;                                 // 当前阶数
    int      Delta;                             // 当前延迟
} WPE_State_FreqBin_t;

// DNN 推理上下文 (零拷贝设计)
typedef struct {
    // 输入/输出张量指针 (由内存池分配，生命周期绑定帧)
    const cplx32_t* input_spec;   // [CH, FREQ]
    cplx32_t*       output_mask;  // [FREQ]
    // 中间激活内存池 (静态分配，避免 malloc)
    int8_t*         activation_pool; 
    size_t          pool_offset;
    // NPU 句柄
    void*           npu_context;
} DNN_Infer_Context_t;

// 全局前端句柄
typedef struct {
    // 配置 (只读，OTA 更新时原子替换)
    const Frontend_Config_t* config;
    // 运行时状态
    WPE_State_FreqBin_t wpe_state[MAX_FREQ_BINS];
    DNN_Infer_Context_t dnn_ctx;
    AlignedRingBuffer_t align_buf;
    // 统计计数器
    uint64_t frame_cnt;
    uint32_t xrun_cnt;
    float    current_rt60_est;
} Audio_Frontend_Handle_t;

#endif