Kıvanç Tatar
Biographic Data
| ID | 4552437 |
|---|---|
| NAME | Kıvanç Tatar |
| GIVEN NAMES | Kıvanç |
| FAMILY NAME | Tatar |
| SIGNATURE | TATAR K |
| AFFILIATIONS | Simon Fraser University, Vancouver, Canada. |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Revising research practices for singing data collection
As AI voice synthesis enables increasingly sophisticated vocal deepfakes and non-consensual voice cloning, the governance, licensing and access of singing datasets has become an urgent concern for data-contributors, who face significant harms from downstream and non-consensual usage of their singing data. Singing datasets are foundational to the development of high fidelity voice AI synthesis, yet current data collection practices pose challenges…
Automatic Synthesizer Preset Generation with PresetGen
We refer the task of finding preset(s) (i.e. set(s) of synthesizer parameters) that approximates a target sound best, as the preset generation problem. PresetGen addresses this problem regarding the real world synthesizer, OP-1. The OP-1 consists of several synthesis blocks, and it is not fully deterministic. We propose and evaluate a solution to preset generation using a multi-objective Non-dominated Sorting-Genetic-Algorithm-II. PresetGen handl…
Automatic Synthesizer Preset Generation with PresetGen
We refer the task of finding preset(s) (i.e. set(s) of synthesizer parameters) that approximates a target sound best, as the preset generation problem. PresetGen addresses this problem regarding the real world synthesizer, OP-1. The OP-1 consists of several synthesis blocks, and it is not fully deterministic. We propose and evaluate a solution to preset generation using a multi-objective Non-dominated Sorting-Genetic-Algorithm-II. PresetGen handl…
Automatic Synthesizer Preset Generation with PresetGen
We refer the task of finding preset(s) (i.e. set(s) of synthesizer parameters) that approximates a target sound best, as the preset generation problem. PresetGen addresses this problem regarding the real world synthesizer, OP-1. The OP-1 consists of several synthesis blocks, and it is not fully deterministic. We propose and evaluate a solution to preset generation using a multi-objective Non-dominated Sorting-Genetic-Algorithm-II. PresetGen handl…
Revising research practices for singing data collection
As AI voice synthesis enables increasingly sophisticated vocal deepfakes and non-consensual voice cloning, the governance, licensing and access of singing datasets has become an urgent concern for data-contributors, who face significant harms from downstream and non-consensual usage of their singing data. Singing datasets are foundational to the development of high fidelity voice AI synthesis, yet current data collection practices pose challenges…
Algorithm (1 work) · Computer Science (1 work) · Data collection (1 work) · Diverse Musicological Studies (1 work) · Downstream (manufacturing) (1 work) · Engineering (1 work) · Ethics and Social Impacts of AI (1 work) · Evolutionary Algorithms and Applications (1 work) · Fidelity (1 work) · Harm (1 work)