Predicting Emotion Perception Across Domains: A Study of Singing and Speaking
Authors: Biqiao Zhang, Emily Mower Provost, Robert Swedberg, Georg Essl
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train regression models using two paradigms: (1) within-domain, in which models are trained and tested on the same domain and (2) cross-domain, in which models are trained on one domain and tested on the other domain. |
| Researcher Affiliation | Academia | Biqiao Zhang, Emily Mower Provost, Robert Swedberg, Georg Essl University of Michigan, Ann Arbor 2260 Hayward St. Ann Arbor, Michigan 48109 |
| Pseudocode | No | The paper describes the methodology in text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making the source code for their described methodology publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | We collected a corpus of speaking and singing performances... The paper describes the creation of its own dataset but does not provide access information (e.g., URL, DOI, or a citation to a publicly available version) for this dataset. |
| Dataset Splits | Yes | We evaluated the models using leave-one-performer-out-cross-validation... The parameters are tuned on the training set using leave-one-utterance-out-cross-validation. |
| Hardware Specification | No | The paper mentions 'Electro-Voice N/D 357 microphone' and 'Canon Vixia HF G10 camcorder' which were used for data collection, but it does not specify any hardware (e.g., GPU, CPU models) used for running the computational experiments or model training. |
| Software Dependencies | No | We used Open SMILE... We extracted the visual features... using CERT... We used ν SV R with a radial basis function kernel implemented in Libsvm. The paper mentions these software tools but does not provide specific version numbers for any of them. |
| Experiment Setup | No | The paper mentions using ν SV R with a radial basis function kernel and details its cross-validation setup, but it does not provide specific hyperparameter values such as learning rates, batch sizes, or optimizer settings for the experiment. |