Tampere University's development dataset contains 40 hours of 10-second audio segments. The audio is organized into 10 distinct acoustic scenes common in urban environments, such as airports, metro stations, and parks. Each scene includes 1440 segments, totaling 240 minutes of audio per scene.
Use Cases
- Train acoustic scene classification models based on labeled 10-second audio segments.
- Benchmark audio event detection algorithms based on the defined urban scene categories.
- Develop urban soundscape analysis tools based on the diverse environmental recordings.
Strengths
- Contains 40 hours of total audio data.
- Each of the 10 acoustic scenes has a balanced 1440 segments (240 minutes).
- Audio segments are a consistent 10 seconds in length.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Tampere University