The zero-crossing rate measures how rapidly the signal changes from positive to negative or vice versa. It’s often used to characterize noisiness in audio. Here’s how you can calculate it:
Calculate zero-crossing rate
zero_crossings_rate = librosa.feature.zero_crossing_rate(y)
plt.figure(figsize=(12, 4))
plt.semilogy(zero_crossings_rate.T)
plt.title(“Zero-Crossing Rate”)
plt.show()
Here is the output:
Figure 10.4 – Zero-crossing rate graph plot
In this code, librosa.feature.zero_crossing_rate() computes the zero-crossing rate, and we use plt.semilogy() to visualize it.
Application: Speech and audio segmentation
Example: The zero-crossing rate is useful for identifying transitions between different sounds. In speech analysis, it can be applied to segment words or phrases.
Spectral contrast
Spectral contrast measures the difference in amplitude between peaks and valleys in the audio spectrum. It can help identify the timbre or texture of the audio signal. Here’s how to compute and display it:
Calculate spectral contrast
spectral_contrast = librosa.feature.spectral_contrast(y=y, sr=sr)
Display the spectral contrast
plt.figure(figsize=(12, 4))
librosa.display.specshow(spectral_contrast, x_axis=’time’)
plt.title(“Spectral Contrast”)
plt.colorbar()
plt.show()
We get the output as follows:
Figure 10.5 – A spectral contrast plot
librosa.feature.spectral_contrast() calculates the spectral contrast, and librosa.display.specshow() displays it.
In this section, we’ve explored more audio analysis features with Librosa, including chroma features, MFCCs, tempo estimation, zero-crossing rate, and spectral contrast. These features are essential tools for understanding and characterizing audio data, whether it’s for music, speech, or any other sound-related applications.
As you continue your journey into audio data analysis, keep experimenting with these features and combine them to solve interesting problems. Audio analysis can be used in music classification, speech recognition, emotion detection, and much more. Have fun exploring the world of audio data! In the following section, let’s dive into the visualization aspect of the audio data.
Application: Environmental sound classification.
Example: Spectral contrast measures the difference in amplitude between peaks and valleys in the spectrum. It can be employed in classifying environmental sounds, distinguishing between, for instance, a bird’s chirp and background noise.
Another example where we use a combination of features is emotion recognition in speech. For instance, a blend of tempo, MFCCs, and zero-crossing rate is utilized, leveraging rhythmic patterns, spectral characteristics, and signal abruptness to enhance the identification of emotional states in spoken language.
Considerations for extracting properties
Model training: In real-world applications, these features are often used as input features for machine learning models. The model is trained to recognize patterns in these features based on labeled data.
Multimodal applications: These features can be combined with other modalities (text, image) for multimodal applications such as video content analysis, where audio features complement visual information.
Real-time processing: Some applications require real-time processing, such as voice assistants using MFCCs for speech recognition or music recommendation systems analyzing tempo and chroma features on the fly.
These examples demonstrate the versatility of audio features in various domains, showcasing their significance in tasks ranging from music classification to emotion recognition in speech.