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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Features of Music From Scratch
Authors: John Thickstun, Zaid Harchaoui, Sham Kakade
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) endto-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio. |
| Researcher Affiliation | Academia | John Thickstun1, Zaid Harchaoui2 & Sham M. Kakade1,2 1 Department of Computer Science and Engineering, 2 Department of Statistics University of Washington Seattle, WA 98195, USA EMAIL, EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper makes the Music Net dataset publicly available at http://homes.cs.washington.edu/ thickstn/musicnet.html (Footnote 2) and mentions a demo using learned features (Footnote 4), but does not explicitly state that the source code for the methodology described in the paper is provided or linked. |
| Open Datasets | Yes | This paper introduces a new large labeled dataset, Music Net, which is publicly available2 as a resource for learning feature representations of music. ... 2http://homes.cs.washington.edu/ thickstn/musicnet.html. |
| Dataset Splits | No | The paper states 'We hold out a test set of 3 recordings for all the results reported in this section' and 'We report the precision and recall corresponding to the best F1-score on validation data'. While a test set is specified and a validation set is mentioned for F1-score optimization, specific percentages, counts, or a detailed splitting methodology for training, validation, and test sets are not provided. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. It only mentions the use of the TensorFlow library. |
| Software Dependencies | No | The paper mentions software like the TensorFlow library, librosa, and mir eval, but does not specify their version numbers. It refers to 'Garritan s Personal Orchestra 4 sample library', but this is a specific library, not a general software dependency with a version. |
| Experiment Setup | Yes | The results reported in Table 3 are achieved with 500 hidden units using a receptive field of 2, 048 samples with an 8-sample stride across a window of 16, 384 samples. These features are grouped into average pools of width 16, with a stride of 8 features between pools. A max-pooling operation yields similar results. The learned representations are optimized for square loss with SGD using the Tensorflow library (Abadi et al.). |