Audio feature extraction is a crucial step in many audio-based applications, including speech recognition, music analysis, and environmental sound classification. In this concept article, we'll explore the basics of audio feature extraction, its importance, and how to implement it using Edge Impulse, particularly for Edge AI use cases. At Edge Impulse, when speaking about feature extraction techniques, we also use the terms DSP (Digital Signal Processing) or pre-processing.
Audio feature extraction involves transforming raw audio signals into a set of meaningful features that can be used for further processing or analysis, including training Edge AI models. These features capture essential characteristics of the audio signal, such as its frequency content, amplitude, and temporal dynamics.
Raw audio data is often too complex and voluminous to be directly used for machine learning tasks. Feature extraction simplifies the audio signal, making it easier to analyze and interpret. This process helps in reducing the dimensionality of the data while retaining the most informative aspects, improving the performance of machine learning models, especially in Edge AI applications where computational resources are limited.
Edge Impulse offers several pre-processing blocks to extract key audio features, simplifying the development process for Edge AI applications:
Spectrogram: A visual representation of the spectrum of frequencies in a signal as it varies with time. It helps in understanding how the energy of the signal is distributed across different frequencies. See the Spectrogram pre-processing block in Edge Impulse.
Mel-Frequency Cepstral Coefficients (MFCC): Represent the short-term power spectrum of a sound, widely used in speech and audio processing due to their effectiveness in capturing the phonetically relevant characteristics of the audio signal. See the MFCC block in Edge Impulse.
Mel-filterbank Energy (MFE): Similar to MFCCs but focuses on the energy in different frequency bands, providing a simpler yet powerful representation of the audio signal. See the MFE block in Edge Impulse.
Note that you can also import your own feature extraction block so you can use it directly in Edge Impulse Studio. See Custom DSP block.
Tutorials:
Keyword Spotting: Responding to your voice
Continuous Audio Classification: Recognize sounds from audio
Blog posts:
Motion feature extraction is a key component in many applications, including activity recognition, gesture control, and vibration analysis. In this concept article, we'll explore the basics of motion feature extraction, its importance, and how to implement it using Edge Impulse, specifically for Edge AI use cases. At Edge Impulse, when speaking about feature extraction techniques, we also use the terms DSP (Digital Signal Processing) or pre-processing.
Motion feature extraction involves transforming raw motion sensor data (such as accelerometer or gyroscope readings) into a set of meaningful features that can be used for further processing or analysis. These features capture essential characteristics of the motion signal, such as its frequency content, amplitude, and temporal dynamics.
Raw motion data is often too complex and voluminous to be directly used for machine learning tasks. Feature extraction simplifies the motion signal, making it easier to analyze and interpret. This process helps in reducing the dimensionality of the data while retaining the most informative aspects, improving the performance of machine learning models, especially in Edge AI applications where computational resources are limited.
Edge Impulse offers a powerful Spectral Features block to extract key motion features, simplifying the development process for Edge AI applications. This block supports two main types of analysis:
Fast Fourier Transform (FFT): Transforms the time-domain signal into the frequency domain, providing information about the signal's frequency content. It is best suited for analyzing repetitive patterns in a signal.
Wavelet Transform: Decomposes the signal into components at various scales, capturing both frequency and temporal information. It works better for complex signals that have transients or irregular waveforms.
Note that you can also import your own feature extraction block so you can use it directly in Edge Impulse Studio. See Custom DSP block.
