For audio search systems, developers typically extract three categories of features: low-level signal properties, mid-level acoustic characteristics, and high-level semantic descriptors. These features enable efficient indexing, similarity comparison, and content-based retrieval across speech, music, or environmental sound datasets. The choice depends on the use case, balancing computational cost with the need for meaningful audio representation.
Low-level features capture raw signal properties. Time-domain metrics like amplitude envelope, zero-crossing rate (detecting how often a signal crosses zero), and root mean square (RMS) energy provide basic loudness and noise characteristics. Frequency-domain features, often derived via Short-Time Fourier Transform (STFT), include spectral centroid (brightness), bandwidth, rolloff (high-frequency cutoff), and Mel-Frequency Cepstral Coefficients (MFCCs). MFCCs—commonly used in speech recognition—compress spectral information into 13-40 coefficients that approximate human hearing perception. For example, a music search system might use spectral contrast to distinguish instruments, while a voice memo app could employ MFCCs for keyword spotting.
Mid-level features describe structural patterns. Beat and tempo detection identifies rhythmic components using onset detection and periodicity analysis. Chroma features map pitches to 12 semitone classes, useful for chord recognition in music (e.g., finding songs with similar harmonic progressions). Pitch histograms and tonal descriptors help classify vocal ranges or instrument types. In environmental sound search, temporal features like modulation spectral density (changes in energy over time) can differentiate between footsteps and clapping. These features often combine low-level data—a drum detection algorithm might first extract spectral flux (sudden energy changes) before applying peak-picking logic.
High-level features abstract semantic meaning. Automatic speech recognition (ASR) converts spoken words to text for transcript-based search. Speaker identification uses Gaussian Mixture Models (GMMs) or neural embeddings to recognize voices. Music information retrieval (MIR) systems might use pre-trained models to extract genre, mood, or instrumentation tags. For instance, a podcast platform could combine ASR transcripts with speaker diarization (identifying who spoke when) to enable precise content searches. These features often rely on machine learning models trained on labeled datasets, transforming raw audio into searchable metadata.