Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Self-Supervised Visual Acoustic Matching
Authors: Arjun Somayazulu, Changan Chen, Kristen Grauman
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed Le MARA model outperforms existing approaches [3, 7, 14] on challenging in-the-wild audio and environments from multiple datasets. Further, to benchmark this task, we introduce a high audio-visual correspondence subset of the AVSpeech [10] video dataset. |
| Researcher Affiliation | Collaboration | Arjun Somayazulu1 Changan Chen1 Kristen Grauman1,2 1UT Austin 2FAIR, Meta |
| Pseudocode | Yes | 7 Supplementary (...) 10. Pseudocode for a discriminator training epoch detailing our reverberator update mechanism (Algorithm 1) |
| Open Source Code | No | Project page: https: //vision.cs.utexas.edu/projects/ss_vam and We plan to release our code to facilitate further research. |
| Open Datasets | Yes | We use two datasets: Sound Spaces-Speech [7] and AVSpeech [10]. |
| Dataset Splits | Yes | Sound Spaces-Speech consists of anechoic speech samples from Libri Speech paired with their acoustically-correct reverberated waveform (rendered using Sound Spaces) in any of 82 unique environments, together with an RGBD image at the listener s position. (...) We use train/val/test splits of 28,853/280/1,489 samples. (...) Our final set consists of 72,615/1,911/1,911 train/val/test samples. |
| Hardware Specification | Yes | Compute All models are trained on 8 NVIDIA Quadro RTX 6000 GPUs. |
| Software Dependencies | No | The paper mentions software components like "speechbrain Metric GAN-U implementation [34]" and "Wave Net-like architecture" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train Le MARA using the combined acoustic residue metric with α = 0.7. (...) We train the reverberators with batch size 4 and a learning rate of 1e-2 in stage (2). During stage (3) fine=tuning, we use batch size 2 and a learning rate of 1e-6. (...) In stage (1) (...) we train with batch size 32. During stage (3) fine-tuning, we use a batch size of 2. G and D are trained with learning rates of 2e-6 and 5e-4 respectively in both stages. (...) We clip each audio sample to 2.56s during training and evaluation. |