diff --git a/energy_based_recorder.py b/energy_based_recorder.py
index 46f5eef..d5358ef 100644
--- a/energy_based_recorder.py
+++ b/energy_based_recorder.py
@@ -81,13 +81,24 @@ class EnergyBasedRecorder:
         # 计算RMS能量
         rms = np.sqrt(np.mean(audio_array ** 2))
         
-        # 更新能量历史
-        self.energy_history.append(rms)
-        if len(self.energy_history) > self.max_energy_history:
-            self.energy_history.pop(0)
+        # 更新能量历史（只在非录音状态下更新，避免语音影响背景噪音计算）
+        if not self.recording:
+            self.energy_history.append(rms)
+            if len(self.energy_history) > self.max_energy_history:
+                self.energy_history.pop(0)
         
         return rms
     
+    def calculate_peak_energy(self, audio_data):
+        """计算峰值能量（辅助判断）"""
+        if len(audio_data) == 0:
+            return 0
+        
+        audio_array = np.frombuffer(audio_data, dtype=np.int16)
+        peak_energy = np.max(np.abs(audio_array))
+        
+        return peak_energy
+    
     def calculate_zero_crossing_rate(self, audio_data):
         """计算零交叉率（辅助判断语音）"""
         if len(audio_data) == 0:
@@ -110,21 +121,21 @@ class EnergyBasedRecorder:
             # 使用最近10个样本的中位数作为背景噪音
             background_energy = np.median(self.energy_history[-10:])
             
-            # 动态阈值：背景噪音 + 25%（比原来的500更敏感）
-            dynamic_threshold = max(50, background_energy * 1.25)
+            # 动态阈值：背景噪音 + 50%（提高敏感性）
+            dynamic_threshold = max(50, background_energy * 1.5)
             
             # 能量条件
             energy_condition = energy > dynamic_threshold
             
-            # 零交叉率条件：语音通常在500-5000 Hz之间
-            # 对于8kHz采样率，ZCR通常在500-2000之间
-            zcr_condition = 500 < zcr < 3000
+            # 零交叉率条件：语音通常在1000-5000 Hz之间
+            # 对于8kHz采样率，ZCR通常在1000-4000之间
+            zcr_condition = 1000 < zcr < 4000
             
             # 同时满足能量和ZCR条件才认为是语音
             return energy_condition and zcr_condition
         else:
             # 初始阶段使用固定阈值
-            return energy > 80  # 更低的初始阈值
+            return energy > 60 and zcr > 1000  # 更严格的初始条件
     
     def get_average_energy(self):
         """获取平均能量水平"""
@@ -332,6 +343,7 @@ class EnergyBasedRecorder:
                 # 计算能量和零交叉率
                 energy = self.calculate_energy(data)
                 zcr = self.calculate_zero_crossing_rate(data)
+                peak_energy = self.calculate_peak_energy(data)
                 
                 # 性能监控
                 self.monitor_performance()
@@ -359,9 +371,10 @@ class EnergyBasedRecorder:
                         print(f"\n⏰ 达到最大录音时间 {self.max_recording_time}秒")
                         self.stop_recording()
                     
-                    # 显示录音状态（包含预录音信息）
+                    # 显示录音状态（包含调试信息）
                     bg_energy = np.median(self.energy_history[-10:]) if len(self.energy_history) >= 10 else 0
-                    status = f"录音中... {recording_duration:.1f}s | 能量: {energy:.0f} | ZCR: {zcr:.0f} | 背景: {bg_energy:.0f}"
+                    is_voice = self.is_voice_active_advanced(energy, zcr)
+                    status = f"录音中... {recording_duration:.1f}s | RMS: {energy:.0f} | 峰值: {peak_energy:.0f} | ZCR: {zcr:.0f} | 语音: {is_voice}"
                     print(f"\r{status}", end='', flush=True)
                 
                 else:
@@ -373,11 +386,12 @@ class EnergyBasedRecorder:
                         # 检测到声音，开始录音
                         self.start_recording()
                     else:
-                        # 显示监听状态（包含缓冲区信息）
+                        # 显示监听状态（包含调试信息）
                         avg_energy = self.get_average_energy()
                         bg_energy = np.median(self.energy_history[-10:]) if len(self.energy_history) >= 10 else 0
                         buffer_usage = len(self.pre_record_buffer) / self.pre_record_max_frames * 100
-                        status = f"监听中... 能量: {energy:.0f} | ZCR: {zcr:.0f} | 背景: {bg_energy:.0f} | 缓冲: {buffer_usage:.0f}%"
+                        is_voice = self.is_voice_active_advanced(energy, zcr)
+                        status = f"监听中... RMS: {energy:.0f} | 峰值: {peak_energy:.0f} | ZCR: {zcr:.0f} | 背景: {bg_energy:.0f} | 语音: {is_voice} | 缓冲: {buffer_usage:.0f}%"
                         print(f"\r{status}", end='', flush=True)
                 
                 # 减少CPU使用