Definition
Audio data collection is the process of gathering raw sound recordings to train and evaluate AI systems. Data may include speech, music, or environmental sounds.
Purpose
The purpose is to create representative datasets that allow audio models to perform reliably across accents, environments, and devices.
Importance
- Essential for training robust speech and audio systems.
- Must consider diversity (languages, conditions) to avoid bias.
- Requires strong privacy and consent measures for recorded voices.
- Quality of collection impacts downstream AI performance.
How It Works
- Define the goals (e.g., speech recognition, sound detection).
- Select recording devices and environments.
- Recruit speakers or gather natural recordings.
- Record audio while controlling noise and quality.
- Store recordings with metadata for later use.
Examples (Real World)
- Google Speech Commands: crowdsourced dataset of spoken commands.
- UrbanSound8K: dataset of labeled environmental sounds.
- LibriSpeech: audiobook-derived corpus for ASR research.
References / Further Reading
- Speech Data Collection Guidelines — Linguistic Data Consortium.
- Datasets for Audio Recognition — Papers With Code.
- ISO/IEC TR 20547-5: Big Data Reference Architecture — ISO.