セグメント1: +0.5ms の誤差
セグメント2: -0.3ms の誤差  
セグメント3: +0.8ms の誤差
...
100セグメント後: 合計 42ms のずれ

# 48kHzサンプリングの場合
time_resolution = 1 / 48000  # 約0.021ms

# 1.5秒を表現しようとすると
ideal_samples = 1.5 * 48000  # 72000サンプル
actual_duration = 72000 / 48000  # 正確に1.5秒

def measure_segment_error(expected_duration, actual_duration):
    """セグメントごとの誤差を測定"""
    error_ms = (actual_duration - expected_duration) * 1000
    return {
        'absolute_error': abs(error_ms),
        'signed_error': error_ms,
        'relative_error': error_ms / (expected_duration * 1000)
    }

def analyze_accumulation_pattern(errors, timestamps):
    """累積パターンを分析"""
    cumulative = np.cumsum(errors)
    
    # 線形モデル: drift = a * time + b
    linear_fit = np.polyfit(timestamps, cumulative, 1)
    
    # 二次モデル: drift = a * time² + b * time + c
    quadratic_fit = np.polyfit(timestamps, cumulative, 2)
    
    # モデルの適合度を評価
    linear_r2 = calculate_r_squared(cumulative, linear_fit)
    quadratic_r2 = calculate_r_squared(cumulative, quadratic_fit)
    
    return {
        'pattern': 'linear' if linear_r2 > 0.9 else 'nonlinear',
        'drift_rate': linear_fit[0],  # ms/秒
        'acceleration': quadratic_fit[0] if quadratic_r2 > linear_r2 else 0
    }

def statistical_analysis(errors):
    """誤差の統計的特性を分析"""
    return {
        'mean': np.mean(errors),
        'std': np.std(errors),
        'skewness': scipy.stats.skew(errors),
        'is_systematic': abs(np.mean(errors)) > np.std(errors) / 2
    }

def static_offset_correction(position, offset_ms, sample_rate):
    """固定オフセットによる補正"""
    offset_samples = int(offset_ms * sample_rate / 1000)
    return position + offset_samples

def linear_correction(position, time, drift_rate, sample_rate):
    """線形ドリフト補正"""
    # drift_rate: ms/秒
    correction_ms = -drift_rate * time
    correction_samples = int(correction_ms * sample_rate / 1000)
    return position + correction_samples

def adaptive_correction(segments, measured_errors):
    """適応的補正アルゴリズム"""
    corrections = []
    accumulated_error = 0
    
    for i, segment in enumerate(segments):
        # 現在までの累積誤差
        accumulated_error += measured_errors[i]
        
        # 将来の誤差を予測
        future_segments = len(segments) - i - 1
        predicted_future_error = np.mean(measured_errors) * future_segments
        
        # 最適な補正量を計算
        optimal_correction = -(accumulated_error + predicted_future_error * 0.5)
        corrections.append(optimal_correction)
    
    return corrections

from scipy.interpolate import UnivariateSpline

def spline_correction(timestamps, cumulative_errors, smoothing_factor=0.1):
    """スプライン補間による滑らかな補正"""
    # 補正曲線を生成
    spline = UnivariateSpline(timestamps, -cumulative_errors, 
                              s=smoothing_factor)
    
    # 各時点での補正値を計算
    corrections = spline(timestamps)
    return corrections

import tensorflow as tf

class DriftPredictionLSTM:
    def __init__(self, sequence_length=10):
        self.model = tf.keras.Sequential([
            tf.keras.layers.LSTM(64, return_sequences=True),
            tf.keras.layers.LSTM(32),
            tf.keras.layers.Dense(16, activation='relu'),
            tf.keras.layers.Dense(1)
        ])
        
    def train(self, historical_errors, timestamps):
        """過去のプロジェクトのドリフトパターンを学習"""
        X, y = self.create_sequences(historical_errors)
        self.model.compile(optimizer='adam', loss='mse')
        self.model.fit(X, y, epochs=100, validation_split=0.2)
    
    def predict_drift(self, recent_errors):
        """次のセグメントのドリフトを予測"""
        return self.model.predict(recent_errors.reshape(1, -1, 1))

class AdaptiveCorrectionRL:
    def __init__(self, state_dim=5, action_dim=11):
        # 状態: [累積誤差, 現在位置, 誤差の分散, 残りセグメント数, 前回の補正量]
        # 行動: -5ms から +5ms の補正（1ms刻み）
        self.q_network = self.build_q_network(state_dim, action_dim)
        self.memory = []
        
    def build_q_network(self, state_dim, action_dim):
        model = tf.keras.Sequential([
            tf.keras.layers.Dense(64, activation='relu', input_shape=(state_dim,)),
            tf.keras.layers.Dense(32, activation='relu'),
            tf.keras.layers.Dense(action_dim)
        ])
        return model
    
    def choose_action(self, state, epsilon=0.1):
        """ε-greedy方策で補正量を決定"""
        if np.random.random() < epsilon:
            return np.random.randint(-5, 6)  # 探索
        else:
            q_values = self.q_network.predict(state.reshape(1, -1))
            return np.argmax(q_values) - 5  # 活用
    
    def update(self, state, action, reward, next_state):
        """Q値を更新"""
        # Deep Q-Learning のアップデート処理
        pass

class WaveformDriftDetector:
    def __init__(self):
        self.model = tf.keras.Sequential([
            # 1D畳み込みで波形の特徴を抽出
            tf.keras.layers.Conv1D(32, 128, activation='relu'),
            tf.keras.layers.MaxPooling1D(4),
            tf.keras.layers.Conv1D(64, 64, activation='relu'),
            tf.keras.layers.GlobalMaxPooling1D(),
            
            # 全結合層で誤差を予測
            tf.keras.layers.Dense(128, activation='relu'),
            tf.keras.layers.Dropout(0.5),
            tf.keras.layers.Dense(1)  # 予測誤差（ms）
        ])
    
    def detect_inherent_delay(self, waveform):
        """波形から固有の遅延を検出"""
        # 音声の立ち上がり特性などから
        # エンコーディングに起因する遅延を推定
        features = self.extract_features(waveform)
        return self.model.predict(features)

class EnsembleDriftCorrector:
    def __init__(self):
        self.correctors = [
            LinearCorrector(),
            SplineCorrector(),
            LSTMPredictor(),
            RLCorrector()
        ]
        self.weights = [0.25, 0.25, 0.25, 0.25]
    
    def correct(self, segment_info):
        """複数の補正手法の重み付き平均"""
        corrections = []
        for corrector, weight in zip(self.correctors, self.weights):
            correction = corrector.predict(segment_info)
            corrections.append(correction * weight)
        
        # 外れ値を除外した平均
        final_correction = np.median(corrections)
        return final_correction
    
    def adapt_weights(self, performance_metrics):
        """各手法の性能に基づいて重みを調整"""
        # メタ学習により最適な重みを学習
        pass