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..
Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data
Authors: Haytham M. Fayek, Anurag Kumar
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the large scale sound events dataset, Audio Set, demonstrate the efficacy of the proposed model, which outperforms the single-modal models, and state-of-the-art fusion and multi-modal models. We achieve a mean Average Precision (m AP) of 46.16 on Audioset, outperforming prior state of the art by approximately +4.35 m AP (relative: 10.4%). |
| Researcher Affiliation | Industry | Facebook Reality Labs, Redmond, WA, USA EMAIL |
| Pseudocode | No | The paper describes model architectures and mathematical formulations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository for the methodology described. |
| Open Datasets | Yes | Audioset [Gemmeke et al., 2017] is the largest dataset for sound events. The dataset provides You Tube videos for 527 sound events. |
| Dataset Splits | Yes | The training set comprises approximately 2 million videos, whereas the evaluation set comprises approximately 20, 000 videos. We sample approximately 25, 000 videos from the training set to use as the validation set. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU, CPU models, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The network in the video model is trained for 20 epochs using the Adam optimizer [Kingma and Ba, 2014] with a mini-batch size of 144. In the fusion experiments, all neural networks are trained using Adam for 100 epochs. The mini-batch size is set to 256. na, nv, and nav are all single layer networks with 512 units and sigmoid activations. |