Driver Frustration Detection from Audio and Video in the Wild
Authors: Irman Abdić, Lex Fridman, Daniel McDuff, Erik Marchi, Bryan Reimer, Björn Schuller
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze a dataset of 20 drivers that contains 596 audio epochs (audio clips, with duration from 1 sec to 15 sec) and 615 video epochs (video clips, with duration from 1 sec to 45 sec). The model was subject-independently trained and tested using 4-fold cross-validation. We achieve an accuracy of 77.4 % for detecting frustration from a single audio epoch and 81.2 % for detecting frustration from a single video epoch. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology (MIT), USA 2Technische Universit at M unchen (TUM), Germany 3Imperial College London (ICL), UK |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions using the 'open-source openSMILE feature extractor', but does not provide access to the authors' own implementation code for their methodology. |
| Open Datasets | No | The paper states: 'The dataset used for frustration detection was collected as part of a study for multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems [Mehler et al., 2015].' This cites a paper describing the collection, but does not provide direct access to the dataset itself or state that it is publicly available. |
| Dataset Splits | Yes | The model was subject-independently trained and tested using 4-fold cross-validation. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | Yes | Acoustic low-level descriptors (LLD) were automatically extracted from the speech waveform on a per-chunk level by using the open-source open SMILE feature extractor in its 2.1 release. We used a Weka 3 implementation of Support Vector Machines (SVMs). |
| Experiment Setup | Yes | We used a Weka 3 implementation of Support Vector Machines (SVMs) with the Sequential Minimal Optimization (SMO), and audio and video features described in 4 [Hall et al., 2009]. We describe a set of SMO complexity parameters as: C 2 {10 4, 5 10 4, 10 3, 5 10 3, ..., 1}. |