Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning
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Abstract (arXiv)
We present Spatial LibriSpeech, a spatial audio dataset with over 650 hours of 19-channel audio, first-order ambisonics, and optional distractor noise. Spatial LibriSpeech is designed for machine learning model training, and it includes labels for source position, speaking direction, room acoustics and geometry. Spatial LibriSpeech is generated by augmenting LibriSpeech samples with 200k+ simulated acoustic conditions across 8k+ synthetic rooms. To demonstrate the utility of our dataset, we train models on four spatial audio tasks, resulting in a median absolute error of 6.60deg on 3D source localization, 0.43m on distance, 90.66ms on T30, and 2.74dB on DRR estimation. We show that the same models generalize well to widely-used evaluation datasets, e.g., obtaining a median absolute error of 12.43deg on 3D source localization on TUT Sound Events 2018, and 157.32ms on T30 estimation on ACE Challenge.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 18 Aug 2023.
Builds on
18 of 41 references.
References · 18
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- 2021Interspeech 2021 Deep Noise Suppression Challengecited by 43
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- 2021Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysiscited by 9
6 more
- 2021PILOT: Introducing Transformers for Probabilistic Sound Event Localizationcited by 7
- 2017The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environmentscited by 3
- 2021Blind Room Parameter Estimation Using Multiple-Multichannel Speech Recordingscited by 3
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- 2020BIRD: Big Impulse Response Datasetcited by 2
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Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 1.
6 papers.
- 2024Learning Spatially-Aware Language and Audio Embeddingscited by 15
- 2024ImmerseDiffusion: A Generative Spatial Audio Latent Diffusion Modelcited by 11
- 2023Novel-View Acoustic Synthesis from 3D Reconstructed Roomscited by 6
- 2025AudioMotionBench: Evaluating Auditory Motion Perception in Audio LLMscited by 2
- 2024Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Featurescited by 1
- 2026RIPPLE: Generating Multi-Channel Phase, Not Recovering Itcited by 0
- 2026RIPPLE: Generating Multi-Channel Phase, Not Recovering It30 Jul 2026
- 2025AudioMotionBench: Evaluating Auditory Motion Perception in Audio LLMs17 Nov 2025
- 2024Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features5 Nov 2024
- 2024ImmerseDiffusion: A Generative Spatial Audio Latent Diffusion Model19 Oct 2024
- 2024Learning Spatially-Aware Language and Audio Embeddings17 Sep 2024
- 2023Novel-View Acoustic Synthesis from 3D Reconstructed Rooms23 Oct 2023