基于音频数据集的性别与年龄组识别：Librosa使用求助（ML新手）

Hey there! Nice job getting to the point of loading and visualizing your audio files—you’ve laid a solid foundation. Let’s walk through exactly what you need to do next to turn those waveform plots into a working classification model.

First, let’s recap where you’re at (I’ve included your code for reference):

for file in range(0,len(audio_files),1):
    audio, sfreq = lr.load(audio_files[file])
    time = np.arange(0,len(audio)) / sfreq
    fig, ax = plt.subplots()
    ax.plot(time,audio)
    ax.set(xlabel='time(s)', ylabel='Sound Amplitude')
    plt.show()

This code lets you inspect raw audio waveforms, but raw audio data isn’t useful for machine learning models—we need to extract meaningful audio features first. Here’s your step-by-step plan:

1. Extract Key Audio Features with Librosa #

Librosa has tons of built-in functions to extract features that capture speaker characteristics (like pitch, timbre, which relate to gender and age). Start with these essential ones:

• MFCCs (Mel-Frequency Cepstral Coefficients): The gold standard for speaker recognition tasks
• Mel Spectrogram: Captures the frequency content over time
• Zero-Crossing Rate: Measures how often the audio signal crosses zero (related to voice roughness)
• RMS Energy: Indicates the loudness of the audio

Here’s a code snippet to extract these features for each audio file:

import librosa as lr
import numpy as np
import pandas as pd

# Initialize lists to store features and labels
features = []
gender_labels = []  # Replace with your actual label data (e.g., 'male'/'female')
age_group_labels = []  # Replace with your actual label data (e.g., '18-25', '26-35')

for file in audio_files:
    audio, sfreq = lr.load(file, sr=None)  # sr=None keeps original sampling rate
    
    # Extract features
    mfccs = lr.feature.mfcc(y=audio, sr=sfreq, n_mfcc=13)
    mfccs_mean = np.mean(mfccs, axis=1)  # Take mean across time to get a fixed-length vector
    
    mel_spec = lr.feature.melspectrogram(y=audio, sr=sfreq)
    mel_spec_mean = np.mean(mel_spec, axis=1)
    
    zcr = lr.feature.zero_crossing_rate(audio)
    zcr_mean = np.mean(zcr)
    
    rms = lr.feature.rms(y=audio)
    rms_mean = np.mean(rms)
    
    # Combine all features into a single vector
    combined_features = np.concatenate([mfccs_mean, mel_spec_mean, [zcr_mean], [rms_mean]])
    features.append(combined_features)
    
    # Add your labels here (make sure the order matches audio_files!)
    # Example:
    # gender_labels.append(your_gender_label_for_this_file)
    # age_group_labels.append(your_age_group_label_for_this_file)

# Convert to a DataFrame for easier handling
feature_df = pd.DataFrame(features)
feature_df['gender'] = gender_labels
feature_df['age_group'] = age_group_labels

2. Preprocess Your Data #

Before training, clean and standardize your data:

• Handle missing values: Drop or impute any rows with missing features/labels
• Standardize features: Use StandardScaler from scikit-learn to scale all features to the same range (critical for most ML models)
• Encode labels: Convert categorical labels (like 'male'/'female') to numerical values using LabelEncoder

Example code for preprocessing:

from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split

# Split features and labels
X = feature_df.drop(['gender', 'age_group'], axis=1)
y_gender = feature_df['gender']
y_age = feature_df['age_group']

# Standardize features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Encode categorical labels
gender_encoder = LabelEncoder()
y_gender_encoded = gender_encoder.fit_transform(y_gender)

age_encoder = LabelEncoder()
y_age_encoded = age_encoder.fit_transform(y_age)

3. Split Data into Train/Test Sets #

Always split your data to evaluate model performance on unseen data:

# For gender classification
X_train_gender, X_test_gender, y_train_gender, y_test_gender = train_test_split(
    X_scaled, y_gender_encoded, test_size=0.2, random_state=42
)

# For age-group classification
X_train_age, X_test_age, y_train_age, y_test_age = train_test_split(
    X_scaled, y_age_encoded, test_size=0.2, random_state=42
)

4. Train a Classification Model #

Start with simple, interpretable models first (like Random Forest or SVM) before moving to deep learning:

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

# Train gender classification model
gender_model = RandomForestClassifier(n_estimators=100, random_state=42)
gender_model.fit(X_train_gender, y_train_gender)

# Predict and evaluate
y_pred_gender = gender_model.predict(X_test_gender)
print(f"Gender Classification Accuracy: {accuracy_score(y_test_gender, y_pred_gender):.2f}")
print("Confusion Matrix:\n", confusion_matrix(y_test_gender, y_pred_gender))

# Train age-group classification model
age_model = RandomForestClassifier(n_estimators=100, random_state=42)
age_model.fit(X_train_age, y_train_age)

y_pred_age = age_model.predict(X_test_age)
print(f"\nAge-Group Classification Accuracy: {accuracy_score(y_test_age, y_pred_age):.2f}")
print("Confusion Matrix:\n", confusion_matrix(y_test_age, y_pred_age))

5. Iterate and Improve #

Once you have a baseline model, try these tweaks to boost performance:

• Add more features: Try chroma features, spectral contrast, or pitch (using lr.pyin to estimate fundamental frequency)
• Try different models: Experiment with SVM, Gradient Boosting, or even deep learning models (like CNNs if you use 2D features like mel spectrograms)
• Hyperparameter tuning: Use GridSearchCV or RandomizedSearchCV to optimize model parameters
• Augment audio data: Add noise, shift pitch, or time-stretch audio to make your model more robust

Quick Notes to Keep in Mind #

• Make sure your labels are correctly aligned with your audio files—this is a common mistake!
• If your dataset is small, consider using transfer learning (e.g., pre-trained audio models tailored for speaker tasks)
• Visualize your features (like using PCA) to see if gender/age groups cluster separately

内容的提问来源于stack exchange，提问作者shivani