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"""
特征提取模块，用于从音频信号中提取声学特征
"""
import numpy as np
import pandas as pd
import librosa
from tqdm import tqdm
from sklearn.preprocessing import StandardScaler
import pickle
import os
def extract_features(audio, sr=22050):
"""
从单个音频信号提取特征（兼容旧函数名）
Args:
audio: 音频信号
sr: 采样率
Returns:
features: 特征字典
"""
return extract_features_single(audio, sr)
def extract_features_single(audio, sr=22050):
"""
从单个音频信号提取特征
Args:
audio: 音频信号
sr: 采样率
Returns:
features: 特征字典
"""
# 确保音频长度统一
max_samples = sr * 5 # 最大5秒
if len(audio) < max_samples:
# 音频太短，用0填充
padding = max_samples - len(audio)
audio = np.pad(audio, (0, padding), 'constant')
else:
# 音频太长，截断
audio = audio[:max_samples]
# 初始化特征字典
features = {}
# 提取ZCR（过零率）
zcr = librosa.feature.zero_crossing_rate(audio)[0]
features['zero_crossing_rate_mean'] = np.mean(zcr)
features['zero_crossing_rate_std'] = np.std(zcr)
features['zero_crossing_rate_max'] = np.max(zcr)
features['zero_crossing_rate_min'] = np.min(zcr)
# 提取RMS（均方根能量）
rms = librosa.feature.rms(y=audio)[0]
features['rms_mean'] = np.mean(rms)
features['rms_std'] = np.std(rms)
features['rms_max'] = np.max(rms)
features['rms_min'] = np.min(rms)
# 提取MFCC（梅尔频率倒谱系数）
mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
for i in range(1, 14):
features[f'mfcc_{i}_mean'] = np.mean(mfccs[i-1])
features[f'mfcc_{i}_std'] = np.std(mfccs[i-1])
features[f'mfcc_{i}_max'] = np.max(mfccs[i-1])
features[f'mfcc_{i}_min'] = np.min(mfccs[i-1])
# 提取频谱质心
spectral_centroid = librosa.feature.spectral_centroid(y=audio, sr=sr)[0]
features['spectral_centroid_mean'] = np.mean(spectral_centroid)
features['spectral_centroid_std'] = np.std(spectral_centroid)
features['spectral_centroid_max'] = np.max(spectral_centroid)
features['spectral_centroid_min'] = np.min(spectral_centroid)
# 提取频谱带宽
spectral_bandwidth = librosa.feature.spectral_bandwidth(y=audio, sr=sr)[0]
features['spectral_bandwidth_mean'] = np.mean(spectral_bandwidth)
features['spectral_bandwidth_std'] = np.std(spectral_bandwidth)
features['spectral_bandwidth_max'] = np.max(spectral_bandwidth)
features['spectral_bandwidth_min'] = np.min(spectral_bandwidth)
# 提取频谱衰减
spectral_rolloff = librosa.feature.spectral_rolloff(y=audio, sr=sr)[0]
features['spectral_rolloff_mean'] = np.mean(spectral_rolloff)
features['spectral_rolloff_std'] = np.std(spectral_rolloff)
features['spectral_rolloff_max'] = np.max(spectral_rolloff)
features['spectral_rolloff_min'] = np.min(spectral_rolloff)
# 提取色度特征
chroma = librosa.feature.chroma_stft(y=audio, sr=sr)
features['chroma_1_mean'] = np.mean(chroma[0])
features['chroma_2_mean'] = np.mean(chroma[1])
features['chroma_3_mean'] = np.mean(chroma[2])
features['chroma_4_mean'] = np.mean(chroma[3])
features['chroma_5_mean'] = np.mean(chroma[4])
# 提取声音谱（Mel频谱）
mel_spec = librosa.feature.melspectrogram(y=audio, sr=sr)
features['mel_spec_mean'] = np.mean(mel_spec)
features['mel_spec_std'] = np.std(mel_spec)
# 提取对数功率谱
log_power = librosa.amplitude_to_db(mel_spec)
features['log_power_mean'] = np.mean(log_power)
features['log_power_std'] = np.std(log_power)
# 添加统计矩
features['audio_mean'] = np.mean(audio)
features['audio_std'] = np.std(audio)
features['audio_skew'] = np.mean((audio - np.mean(audio))**3) / (np.std(audio)**3)
features['audio_kurtosis'] = np.mean((audio - np.mean(audio))**4) / (np.std(audio)**4) - 3
# 估计音高
pitches, magnitudes = librosa.piptrack(y=audio, sr=sr)
pitches_mean = []
for t in range(pitches.shape[1]):
idx = np.argmax(magnitudes[:, t])
pitch = pitches[idx, t]
if pitch > 0: # 过滤掉静音帧
pitches_mean.append(pitch)
if pitches_mean: # 确保有有效的音高值
features['pitch_mean'] = np.mean(pitches_mean)
features['pitch_std'] = np.std(pitches_mean) if len(pitches_mean) > 1 else 0
features['pitch_max'] = np.max(pitches_mean)
features['pitch_min'] = np.min(pitches_mean) if len(pitches_mean) > 0 else 0
else:
features['pitch_mean'] = 0
features['pitch_std'] = 0
features['pitch_max'] = 0
features['pitch_min'] = 0
# 提取调谐偏差
tuning_offset = librosa.estimate_tuning(y=audio, sr=sr)
features['tuning_offset'] = tuning_offset
# 新增特征 2：提取光谱平坦度指标
spectral_flatness = librosa.feature.spectral_flatness(y=audio)[0]
features['spectral_flatness_mean'] = np.mean(spectral_flatness)
features['spectral_flatness_std'] = np.std(spectral_flatness)
features['spectral_flatness_max'] = np.max(spectral_flatness)
features['spectral_flatness_min'] = np.min(spectral_flatness)
# 新增特征 3：提取光谱对比度指标
spectral_contrast = librosa.feature.spectral_contrast(y=audio, sr=sr)
# 为每个频带提取统计特征
for i in range(spectral_contrast.shape[0]):
features[f'spectral_contrast_{i+1}_mean'] = np.mean(spectral_contrast[i])
features[f'spectral_contrast_{i+1}_std'] = np.std(spectral_contrast[i])
# 光谱对比度的总体统计
features['spectral_contrast_mean'] = np.mean(spectral_contrast)
features['spectral_contrast_std'] = np.std(spectral_contrast)
# 新增特征 4：梅尔频率特征扩展（针对图片中提到的梅尔频率）
mfcc_delta = librosa.feature.delta(mfccs)
mfcc_delta2 = librosa.feature.delta(mfccs, order=2)
# 添加一阶差分特征
for i in range(1, 14):
features[f'mfcc_{i}_delta_mean'] = np.mean(mfcc_delta[i-1])
features[f'mfcc_{i}_delta_std'] = np.std(mfcc_delta[i-1])
# 添加二阶差分特征
for i in range(1, 14):
features[f'mfcc_{i}_delta2_mean'] = np.mean(mfcc_delta2[i-1])
features[f'mfcc_{i}_delta2_std'] = np.std(mfcc_delta2[i-1])
return features
def extract_features_batch(audio_list):
"""
批量提取音频特征
Args:
audio_list: 音频信号列表
Returns:
features_list: 特征字典列表
"""
features_list = []
for i, audio in enumerate(tqdm(audio_list, desc="提取特征")):
try:
features = extract_features_single(audio)
features_list.append(features)
except Exception as e:
print(f"处理第{i}个音频时出错: {e}")
# 添加空特征字典，避免索引错误
features_list.append({})
return features_list
def features_to_matrix(features_list, feature_names=None):
"""
将特征字典列表转换为特征矩阵
Args:
features_list: 特征字典列表
feature_names: 特征名称列表，如果为None则从第一个非空字典中获取
Returns:
X: 特征矩阵
feature_names: 特征名称列表
"""
# 如果没有提供特征名称，从第一个非空字典中获取
if feature_names is None:
for features in features_list:
if features: # 非空字典
feature_names = list(features.keys())
break
if feature_names is None:
raise ValueError("所有特征字典都是空的，无法确定特征名称")
# 创建特征矩阵
X = np.zeros((len(features_list), len(feature_names)))
for i, features in enumerate(features_list):
if not features: # 空字典
# 填充为0，或者可以使用平均值等
continue
for j, name in enumerate(feature_names):
if name in features:
X[i, j] = features[name]
return X, feature_names
def normalize_features(X_train, X_val, X_test):
"""
标准化特征
Args:
X_train: 训练集特征矩阵
X_val: 验证集特征矩阵
X_test: 测试集特征矩阵
Returns:
X_train_norm: 标准化后的训练集
X_val_norm: 标准化后的验证集
X_test_norm: 标准化后的测试集
"""
# 初始化标准化器
scaler = StandardScaler()
# 使用训练集拟合标准化器
scaler.fit(X_train)
# 转换所有数据集
X_train_norm = scaler.transform(X_train)
X_val_norm = scaler.transform(X_val)
X_test_norm = scaler.transform(X_test)
# 确保输出目录存在
output_dir = 'output/emotion_model'
os.makedirs(output_dir, exist_ok=True)
# 保存标准化器
with open(os.path.join(output_dir, 'feature_scaler.pkl'), 'wb') as f:
pickle.dump(scaler, f)
return X_train_norm, X_val_norm, X_test_norm
def normalize_features_with_params(X, scaler):
"""
使用给定的缩放参数标准化特征
Args:
X: 特征矩阵
scaler: 已经拟合的标准化器
Returns:
X_norm: 标准化后的特征矩阵
"""
return scaler.transform(X)
def reshape_for_lstm(X):
"""
将特征矩阵重塑为LSTM输入格式
Args:
X: 特征矩阵，或特征矩阵列表
Returns:
X_reshaped: 重塑后的特征矩阵
"""
# 如果输入是列表，转换为数组
if isinstance(X, list):
X = np.array(X)
# 添加时间步维度
if len(X.shape) == 2:
return X.reshape(X.shape[0], 1, X.shape[1])
# 如果已经是3D，直接返回
return X