Jikan to Kukan: A Hands-On Musical Experience in AI, Games and Art
Authors: Georgia Martins, Mário Escarce Junior, Leandro Soriano Marcolino
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this demonstration, we allow the conference participants to play Jikan to Kukan , and have their own unique musical experience. |
| Researcher Affiliation | Collaboration | 1 Phersu Interactive, Belo Horizonte, Brazil {georgia,mario}@phersu.com.br 2Computer Science Department, University of Southern California, Los Angeles, CA, USA sorianom@usc.edu |
| Pseudocode | No | The paper includes Table 1 which defines a building block for notes, but it does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | as shown in https://youtu.be/sj KY pfy NTc) This link is to a YouTube video demonstrating the game, not to its source code. The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper describes a game system and its interactive elements, but it does not mention the use of any external, publicly available, or open datasets for training or any other purpose, nor does it provide access information for such. |
| Dataset Splits | No | The paper does not mention using any datasets for which training, validation, or test splits would be required or specified. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud instance types used for running the system or its demonstration. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | No | The paper describes the design and mechanics of the game (e.g., "The game is controlled by a finite state machine"), but it does not provide specific details about an experimental setup, such as hyperparameter values, training configurations, or system-level settings typically found in machine learning experiments. |