Sample-Constrained Black Box Optimization for Audio Personalization
Authors: Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 {rr30,yulinlw2,croy}@illinois.edu |
| 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. |