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..
Low-Rank Time-Frequency Synthesis
Authors: Cédric Févotte, Matthieu Kowalski
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We describe two expectation-maximization algorithms for estimation in the new model and report audio signal processing results with music decomposition and speech enhancement. |
| Researcher Affiliation | Academia | C edric F evotte Laboratoire Lagrange (CNRS, OCA & Universit e de Nice) Nice, France EMAIL Matthieu Kowalski Laboratoire des Signaux et Syst emes (CNRS, Sup elec & Universit e Paris-Sud) Gif-sur-Yvette, France EMAIL |
| Pseudocode | Yes | E-step: z(i) = E{z|x, λ(i)} = α(i) + β λ(i) Φ (x Φα(i)) (16) M-step: (f, n), α(i+1) fn = v(i) fn v(i) fn + β z(i) fn (17) (W(i+1), H(i+1)) = arg min W,H 0 fn DIS |α(i+1) fn |2|[WH]fn (18) T x Φα(i+1) 2 F (19) |
| Open Source Code | No | The paper mentions "Sound examples are provided in the supplementary material." but does not state that the source code for the methodology is openly available or provide a link to it. |
| Open Datasets | Yes | The training data, with sampling rate 16k Hz, is extracted from the TIMIT database [12]. |
| Dataset Splits | No | The paper mentions training and test data but does not explicitly describe a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions "Large Time-Frequency Analysis Toolbox (LTFAT) [7]" but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We use a 2048 samples-long ( 46 ms) Hann window for the tonal layer, and a 128 samples-long ( 3 ms) Hann window for the transient layer, both with a 50% time overlap. The number of latent components in the two layers is set to K = 3. The two t-f bases are Gabor frames with Hann window of length 512 samples ( 32 ms) for the tonal layer and 32 samples ( 2 ms) for the transient layer, both with 50% overlap. The hyperparameter λ is gradually decreased to a negligible value during iterations (resulting in a negligible residual e), a form of warm-restart strategy [13]. Wtrain tonal and Wtrain transient are fixed pre-trained dictionaries of dimension K = 500, obtained from 30 min of training speech containing male and female speakers. The noise dictionaries Wnoise tonal and Wnoise transient are learnt from the noisy data, using K = 2. |