Audioannotation

Audioannotation refers to the process of adding metadata to audio data to enable analysis and machine understanding. This metadata may include transcripts, speaker labels, event timings, and acoustic properties, all aligned to the audio signal in time. It provides a structured representation of what is present in the audio, when it occurs, and potentially how it was produced.

Annotation types include transcription (word-level or phonetic), speaker diarization (determining who spoke when), emotion or sentiment labeling, instrument and genre tags in music, environmental sound labels, and segmentation into labeled intervals with onset and offset times. Annotations may also record acoustic features such as pitch, energy, or spectral descriptors, supporting downstream tasks like feature-based classification or clustering.

Workflows involve data collection, developing annotation guidelines, training annotators, distributing tasks, and performing quality control, often using multiple annotators and computing inter-annotator agreement. Adjudication resolves disagreements, and revisions are tracked to maintain versioning. Time-stamped annotations rely on precise onset/offset timing, typically to the nearest millisecond or frame, and may include confidence scores.

Common formats and tools include TextGrid (Praat), ELAN (.eaf), JSON, CSV, and WebVTT. Widely used annotation tools include Praat, ELAN, Transcriber, and other labelers; semi-automatic options may leverage forced alignment or machine-assisted labeling to speed up the process.

Applications span automatic speech recognition, speaker recognition, music information retrieval, sound event detection, and content-based indexing. Challenges include subjectivity and consistency across annotators, privacy concerns, annotation cost, data imbalance, and the need for standardized guidelines and interoperable formats.