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..
Sample-Constrained Black Box Optimization for Audio Personalization
Authors: Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results are validated through simulations and real world experiments, where volunteers gave feedback on music/speech audio and were able to achieve high satisfaction levels. |
| Researcher Affiliation | Academia | Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Descriptions are in prose. |
| Open Source Code | No | The paper states 'The audio demos at various stages of the optimization are made available2. 2https://oraclebo.github.io/', but this link refers to audio demos and not explicitly the source code for the methodology described. |
| Open Datasets | Yes | We played audio that was deliberately corrupted with hearing loss profiles from the public hearing-loss dataset in NHANES (Salmon et al. 2022). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits, such as percentages, sample counts, or explicit splitting methodologies. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Parameters in BAF and DMS modules include: N, dimension of the filter; d, dimension of the embedding; q, number of candidates BAF outputs, and σ, the dimensional variance in DMS. |