Introduction to Audioflux

Audioflux is a powerful Python library designed for comprehensive audio processing. It provides a plethora of APIs that can be used for various tasks including audio analysis, feature extraction, and visualization. In this blog post, we will cover dozens of useful APIs that Audioflux offers along with practical code snippets to help you get started.

Getting Started with Audioflux

  pip install Audioflux  

Loading an Audio File

  import audioflux as af audio_path = 'path_to_your_audio_file.wav' waveform, sr = af.read(audio_path)  

Extracting MFCC (Mel-Frequency Cepstral Coefficients)

  mfcc = af.feature.mfcc(waveform, sr) print(mfcc.shape)  

Performing STFT (Short-Time Fourier Transform)

  stft = af.transform.stft(waveform, sr) print(stft.shape)  

Spectrogram Visualization

  af.plot.spectrogram(stft, sr)  

Zero Crossing Rate

  zcr = af.feature.zero_crossing_rate(waveform) print(zcr.shape)  

Chroma Feature Extraction

  chroma = af.feature.chroma(waveform, sr) print(chroma.shape)  

Example Application: Genre Classification

Let’s create a simple genre classification application using Audioflux.

  import audioflux as af from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier import numpy as np
# Load audio file audio_path = 'path_to_your_audio_file.wav' waveform, sr = af.read(audio_path)
# Extract features mfcc = af.feature.mfcc(waveform, sr) chroma = af.feature.chroma(waveform, sr) zcr = af.feature.zero_crossing_rate(waveform)
# Combine features features = np.hstack((mfcc, chroma, zcr))
# Dummy labels (0: Class A, 1: Class B) labels = np.array([0, 1])
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
# Train classifier clf = RandomForestClassifier() clf.fit(X_train, y_train)
# Predict predictions = clf.predict(X_test) print(predictions)  

Conclusion

Audioflux is a versatile library that offers a range of APIs for audio processing tasks. This blog post covered several key features and provided code snippets to help you get started with your audio projects. We also created a simple genre classification application to demonstrate the library’s capabilities.

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