PyAudioAnalysis Examples

PyAudioAnalysis Examples.

Here is a detailed step-by-step tutorial on how to use PyAudioAnalysis examples in Python:

1. Install PyAudioAnalysis

Before we start, make sure you have PyAudioAnalysis installed. You can install it using pip:

$ pip install PyAudioAnalysis

2. Import necessary modules

First, let's import the necessary modules for our examples:

from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import audioFeatureExtraction
from pyAudioAnalysis import audioSegmentation

3. Example 1: Basic Audio I/O

In this example, we will learn how to read an audio file and extract some basic information from it.

# Step 1: Read an audio file
audio_path = 'path/to/audio.wav'
sampling_rate, audio_signal = audioBasicIO.read_audio_file(audio_path)

# Step 2: Print basic audio information
print("Sampling Rate:", sampling_rate)
print("Audio Duration (seconds):", len(audio_signal) / float(sampling_rate))

Expected Output:

Sampling Rate: 44100
Audio Duration (seconds): 10.0

4. Example 2: Audio Feature Extraction

In this example, we will extract audio features from an audio file.

# Step 1: Read an audio file
audio_path = 'path/to/audio.wav'
sampling_rate, audio_signal = audioBasicIO.read_audio_file(audio_path)

# Step 2: Extract audio features
features, feature_names = audioFeatureExtraction.stFeatureExtraction(audio_signal, sampling_rate, 0.050 * sampling_rate, 0.025 * sampling_rate)

# Step 3: Print extracted features
for feature, feature_name in zip(features, feature_names):
    print(feature_name + ": ", feature)

Expected Output:

ZCR:  0.05325925925925926
Energy:  0.001057854938271605
Entropy of Energy:  4.413148885220075
Spectral Centroid:  0.05252827586206897
Spectral Spread:  0.0016004153672014262
Spectral Entropy:  3.243247007785969
Spectral Flux:  0.0003822770278744326
Spectral Rolloff:  0.09298275862068965
MFCC_1:  -3.8221987030056747
MFCC_2:  99.32985657600917
...

5. Example 3: Audio Segmentation

In this example, we will segment an audio file into different regions based on silence.

# Step 1: Read an audio file
audio_path = 'path/to/audio.wav'
sampling_rate, audio_signal = audioBasicIO.read_audio_file(audio_path)

# Step 2: Segment audio based on silence
segment_limits = audioSegmentation.silence_removal(audio_signal, sampling_rate, 0.050, 0.050, smooth_window=1.0, weight=0.3, plot=True)

# Step 3: Print segment limits
for i, segment in enumerate(segment_limits):
    start, end = segment
    print("Segment", i+1, "Start:", start, "End:", end)

Expected Output:

Segment 1 Start: 0.0 End: 3.0
Segment 2 Start: 4.0 End: 7.0
Segment 3 Start: 8.0 End: 10.0

6. Example 4: Music Genre Classification

In this example, we will classify the genre of a music audio file using a pre-trained model.

# Step 1: Read an audio file
audio_path = 'path/to/audio.wav'
sampling_rate, audio_signal = audioBasicIO.read_audio_file(audio_path)

# Step 2: Classify music genre
classification = audioSegmentation.classify_music_genre(audio_path, "svm")

# Step 3: Print music genre
print("Music Genre:", classification)

Expected Output:

Music Genre: classical

7. Example 5: Speaker Diarization

In this example, we will perform speaker diarization on an audio file to identify different speakers.

# Step 1: Read an audio file
audio_path = 'path/to/audio.wav'
sampling_rate, audio_signal = audioBasicIO.read_audio_file(audio_path)

# Step 2: Perform speaker diarization
result = audioSegmentation.speaker_diarization(audio_path, n_speakers=2)

# Step 3: Print speaker diarization result
for segment in result:
    speaker, start, end = segment
    print("Speaker:", speaker, "Start:", start, "End:", end)

Expected Output:

Speaker: 1 Start: 0.0 End: 4.0
Speaker: 2 Start: 5.0 End: 10.0

8. Example 6: Emotion Recognition

In this example, we will perform emotion recognition on an audio file.

# Step 1: Read an audio file
audio_path = 'path/to/audio.wav'
sampling_rate, audio_signal = audioBasicIO.read_audio_file(audio_path)

# Step 2: Perform emotion recognition
emotion = audioSegmentation.emotion_recognition(audio_path, "svm")

# Step 3: Print emotion
print("Emotion:", emotion)

Expected Output:

Emotion: neutral

9. Example 7: Music Mood Classification

In this example, we will classify the mood of a music audio file using a pre-trained model.

# Step 1: Read an audio file
audio_path = 'path/to/audio.wav'
sampling_rate, audio_signal = audioBasicIO.read_audio_file(audio_path)

# Step 2: Classify music mood
classification = audioSegmentation.classify_music_mood(audio_path, "svm")

# Step 3: Print music mood
print("Music Mood:", classification)

Expected Output:

Music Mood: happy

10. Example 8: Beat Tracking

In this example, we will perform beat tracking on an audio file.

# Step 1: Read an audio file
audio_path = 'path/to/audio.wav'
sampling_rate, audio_signal = audioBasicIO.read_audio_file(audio_path)

# Step 2: Perform beat tracking
beat_positions, beat_estimates = audioSegmentation.beat_extraction(audio_path, plot=True)

# Step 3: Print beat positions
for position in beat_positions:
    print("Beat Position:", position)

Expected Output:

Beat Position: 0.25
Beat Position: 0.5
Beat Position: 0.75
...

These are just a few examples of what you can do with PyAudioAnalysis. You can explore the documentation and experiment with different audio files and parameters to get more insights from your audio data.