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@@ -8,22 +8,22 @@ tags:
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  - Noise Collection
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  - background noise
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  ---
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- ## Speech-Free Background Noise Dataset — Real-World, Non-Synthetic (50+ Hours)
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- # Dataset summary
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  50+ hours of real-world urban environmental/ambient background noise (field recordings) without intelligible speech (speech-free), from three scenes: airport, street, subway. The dataset is non-synthetic and intended for speech enhancement via noise augmentation and sound event detection (SED) as “clean background”/negative class
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- # Purpose and usage scenarios
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  - Speech enhancement: adding ambient/background noise to clean speech
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  - Sound Event Detection: background samples without target events/speech; negative samples and false alarm rate estimation
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  - Filtering/noise reduction: training noise reduction models without the risk of intelligible speech leakage
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- # Noise Environments
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  - airport: terminals, corridors, gates, baggage areas — ambient/background noise
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  - street: sidewalks and roadways; traffic, wind, footsteps, street music as indistinct background ambient noise
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  - subway: platform, train car, passageways; braking/acceleration, tunnel rumble, doors, announcements as indistinct background noise
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- # Features:
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  - Only real-world field recordings. No synthetic mixes; non-synthetic source audio
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  - No intelligible speech (speech-free). Natural crowd murmur allowed only when no single utterance is intelligible
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  - Only noise. Music, dominant speech, and close-up announcements are excluded
 
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  - Noise Collection
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  - background noise
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  ---
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+ # Speech-Free Background Noise Dataset — Real-World, Non-Synthetic (50+ Hours)
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+ ## Dataset summary
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  50+ hours of real-world urban environmental/ambient background noise (field recordings) without intelligible speech (speech-free), from three scenes: airport, street, subway. The dataset is non-synthetic and intended for speech enhancement via noise augmentation and sound event detection (SED) as “clean background”/negative class
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+ ## Purpose and usage scenarios
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  - Speech enhancement: adding ambient/background noise to clean speech
18
  - Sound Event Detection: background samples without target events/speech; negative samples and false alarm rate estimation
19
  - Filtering/noise reduction: training noise reduction models without the risk of intelligible speech leakage
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+ ## Noise Environments
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  - airport: terminals, corridors, gates, baggage areas — ambient/background noise
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  - street: sidewalks and roadways; traffic, wind, footsteps, street music as indistinct background ambient noise
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  - subway: platform, train car, passageways; braking/acceleration, tunnel rumble, doors, announcements as indistinct background noise
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+ ## Features:
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  - Only real-world field recordings. No synthetic mixes; non-synthetic source audio
28
  - No intelligible speech (speech-free). Natural crowd murmur allowed only when no single utterance is intelligible
29
  - Only noise. Music, dominant speech, and close-up announcements are excluded