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..
Optimal spectral transportation with application to music transcription
Authors: Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A very fast and simple decomposition algorithm that achieves state-of-the-art performance on real musical data. and 6 Experiments |
| Researcher Affiliation | Academia | Rémi Flamary Université Côte d Azur, CNRS, OCA EMAIL Cédric Févotte CNRS, IRIT, Toulouse EMAIL Nicolas Courty Université de Bretagne Sud, CNRS, IRISA EMAIL Valentin Emiya Aix-Marseille Université, CNRS, LIF EMAIL |
| Pseudocode | No | The paper describes the proposed algorithm steps in prose but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | A Python implementation of OST and real-time demonstrator are available at https://github. com/rflamary/OST |
| Open Datasets | Yes | We consider in this section the transcription of a selection of real piano recordings, obtained from the MAPS dataset (Emiya et al., 2010). |
| Dataset Splits | Yes | Half of the recording is used for validation of the hyper-parameters and the other half is used as test data. |
| Hardware Specification | No | The paper mentions results were run 'on an average desktop PC' but does not provide specific hardware details such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For PLCA, we validated 4 and 3 values of the width and amplitude dampening of the Gaussian kernels used to synthesise the dictionary. For OST, we set ϵ = qϵ0 in Eq. (4), which was found to satisfactorily improve the discrimination of octaves increasingly with frequency, and validated 5 orders of magnitude of ϵ0. For OSTe, we additionally validated 4 orders of magnitude of λe. |