Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Brains on Beats
Authors: Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We developed task-optimized deep neural networks (DNNs) that achieved state-of-the-art performance in different evaluation scenarios for automatic music tagging. These DNNs were subsequently used to probe the neural representations of music. Representational similarity analysis revealed the existence of a representational gradient across the superior temporal gyrus (STG). Representational similarity analysis revealed the existence of a representational gradient across the superior temporal gyrus (STG). |
| Researcher Affiliation | Academia | Umut Güçlü Radboud University, Donders Institute for Brain, Cognition and Behaviour Nijmegen, the Netherlands EMAIL Jordy Thielen Radboud University, Donders Institute for Brain, Cognition and Behaviour Nijmegen, the Netherlands EMAIL Michael Hanke Otto-von-Guericke University Magdeburg Center for Behavioral Brain Sciences Magdeburg, Germany EMAIL Marcel A. J. van Gerven Radboud University, Donders Institute for Brain, Cognition and Behaviour Nijmegen, the Netherlands EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Figure 1 shows a model architecture diagram, not pseudocode. |
| Open Source Code | No | The paper mentions that 'The DNN models were implemented in Keras [30]' with a link to the Keras library. However, it does not provide concrete access to the specific source code for the methodology described in this paper or state that their code is open-sourced. |
| Open Datasets | Yes | We used the Magna Tag ATune dataset [22] for DNN estimation. We used the existing studyforrest dataset [23] for representational similarity analysis. |
| Dataset Splits | Yes | Parts 1-12 were used for training, part 13 was used for validation and parts 14-16 were used for testing. |
| Hardware Specification | No | The paper mentions 'Ultra-high-field (7 Tesla) f MRI images were collected using a Siemens MAGNETOM scanner' for data acquisition, but it does not provide specific hardware details (like GPU/CPU models, processors, or memory) used for running the deep neural network experiments. |
| Software Dependencies | No | The paper states, 'The DNN models were implemented in Keras [30].' Reference [30] is 'F. Chollet, Keras. https://github.com/fchollet/keras, 2015.' While it names Keras and its publication year, it does not provide specific version numbers for Keras or any other ancillary software components (e.g., 'Keras 1.2.0', 'Python 3.x'). |
| Experiment Setup | Yes | We used Adam [28] with parameters α = 0.0002, β1 = 0.5, β2 = 0.999, ϵ = 1e 8 and a mini batch size of 36 to train the models by minimizing the binary cross-entropy loss function. Initial model parameters were drawn from a uniform distribution as described in [29]. Songs in each training mini-batch were randomly cropped to six seconds (96000 samples). The epoch in which the validation performance was the highest was taken as the final model (53, 12 and 12 for T, F and TF models, respectively). |