When extracting features from audio signals for search purposes, three main categories of features are commonly used: time-domain features, frequency-domain features, and perceptual features. Each category provides different insights into the audio signal that can enhance search capabilities, such as identifying keywords, music genres, or specific sounds.
Time-domain features include basic characteristics derived from the raw waveform of the audio. One common feature is the zero-crossing rate, which measures how often the audio waveform changes sign. This can be useful for distinguishing between different types of sounds. Another important feature is the envelope of the waveform, which captures the overall amplitude variations over time. Developers can utilize these features for applications like voice activity detection or to identify sharp transients in music.
Frequency-domain features are obtained through techniques like the Fast Fourier Transform (FFT), which converts the audio signal from the time domain to the frequency domain. One popular feature is the spectrogram, which visually represents how energy is distributed across different frequencies over time. This is particularly useful in speech recognition and music classification. Additionally, Mel-frequency cepstral coefficients (MFCCs) are often used to capture the audio signal in a way that aligns with human perception, making them valuable for tasks such as speaker identification or emotion detection in speech. Finally, perceptual features consider how humans perceive sounds, measuring aspects like loudness or pitch, which further enhance the relevance of the audio data in search applications.