The Scaffolded Sound Beehive
Authors: AnneMarie Maes
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The installation includes an experiment in using Deep Learning to interpret the activities in the hive based on sound and microclimate recording. As a special contribution to IJCAI, a project was carried out to use Deep Learning techniques using massive data from sound and microclimate in order to get an interpretation of hive activity. |
| Researcher Affiliation | Collaboration | Anne Marie Maes OKNO Brussels Urban Bee Lab Vlaamse Steenweg 66 1000 Brussels Belgium. annemie@okno.be. The Brussels Urban Bee Lab [BUBL] is an independent international collective of artists, scientists, beekeepers, technicians and creative people. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'open source digital fabrication' for the hive structure, but it does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the Deep Learning or pattern recognition methodologies described. |
| Open Datasets | No | The paper describes data collected from urban beehives ('recordings of actual bee and environmental sounds', 'images were recorded with an infrared camera'). However, it does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper mentions 'Raspberry Pi computers' and 'piezo sensors are connected to a small mixing panel with ampliļ¬cation'. However, it does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) for the Deep Learning processing or general computational experiments. |
| Software Dependencies | No | The paper mentions general software categories like 'sophisticated pattern recognition, AI technologies, and soniļ¬cation and computer graphics software', and specific tools like 'Max/MSP' and 'Blender'. However, it does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment, particularly for the Deep Learning components. |
| Experiment Setup | No | The paper describes the overall setup of the artistic installation and the general process of data transformation for Deep Learning. However, it does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) for the Deep Learning components in the main text. |