Background Noise Detection Dataset
50+ hours of speech-free urban noise recordings. Airport, street, subway environments
Check samples on Kaggle
Introduction
The Background Noise Detection Dataset provides a comprehensive collection of real-world urban ambient noise for developing robust speech processing and acoustic analysis systems. This dataset addresses the critical need for speech-free environmental audio in speech enhancement, sound event detection, and noise reduction applications. All recordings are authentic field captures with no synthetic audio, ensuring models trained on this data perform reliably in real-world deployment scenarios
Dataset summary
- 50+ hours of speech-free audio recordings from authentic urban environments
- Three distinct noise categories: Airport, Street, and Subway
- Real-world field recordings with no synthetic or artificially generated audio
- Speech-free guarantee: recordings exclude intelligible speech, music, and prominent announcements
- Ideal for training applications: speech enhancement augmentation, sound event detection, and noise reduction model development
Noise Environments
- Airport: terminals, corridors, gates, baggage areas – ambient/background noise
- Street: sidewalks and roadways, traffic, wind, footsteps, street music as indistinct background ambient noise
- Subway: platform, train car, passageways, braking/acceleration, tunnel rumble, doors, announcements as indistinct background noise
Features
- Only real-world field recordings. No synthetic mixes; non-synthetic source audio
- No intelligible speech (speech-free). Natural crowd murmur allowed only when no single utterance is intelligible
- Only noise. Music, dominant speech, and close-up announcements are excluded
Source and collection methodology
The audio was captured using professional field recording equipment across authentic urban locations including airports, city streets, and subway systems. All recordings were collected in real-world conditions to preserve natural acoustic characteristics, reverberation patterns, and environmental dynamics. Each recording was carefully reviewed to ensure complete absence of intelligible speech, preventing speech leakage in noise reduction and enhancement applications
Use cases and applications
- Speech enhancement: adding ambient/background noise to clean speech
- Sound Event Detection: background samples without target events/speech; negative samples and false alarm rate estimation
- Filtering/noise reduction: training noise reduction models without the risk of intelligible speech leakage
Who is this for?
AI/ML teams – Train robust speech enhancement and noise reduction models with authentic environmental audio
Voice AI developers – Augment training data for ASR and voice assistant systems in real-world conditions
Audio software companies – Build noise cancellation and filtering solutions without speech leakage risks
Legal & Compliance
We prioritize data privacy, ethical AI development, and regulatory compliance. Our Background Noise Detection Dataset is collected and processed in full accordance with global data protection standards including GDPR, ensuring legality, security, and responsible AI practices
Sample dataset
A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below
Contact us
Tell us about yourself, and get access to free samples of the dataset
