An Infinite Hidden Markov Model With Similarity-Biased Transitions
Authors: Colin Reimer Dawson, Chaofan Huang, Clayton T. Morrison
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the model and inference method on a speaker diarization task and a harmonic parsing task using fourpart chorale data, as well as on several synthetic datasets, achieving favorable comparisons to existing models. |
| Researcher Affiliation | Academia | 1 Oberlin College, Oberlin, OH, USA 2The University of Arizona, Tucson, AZ, USA. |
| Pseudocode | No | The paper describes the Gibbs sampling algorithm in prose but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | Code and additional details are available at http://colindawson.net/hdp-hmm-lt/ |
| Open Datasets | Yes | The data was constructed using audio signals collected from the PASCAL 1st Speech Separation Challenge2. The underlying signal consisted of D = 16 speaker channels recorded at each of T = 2000 time steps...2http://laslab.org/Speech Separation Challenge/ The data was a corpus of 217 four-voice major key chorales by J.S. Bach from music214...4http://web.mit.edu/music21 |
| Dataset Splits | Yes | The data was a corpus of 217 four-voice major key chorales by J.S. Bach from music21, 200 of which were randomly selected as a training set, with the other 17 used as a test set to evaluate surprisal (marginal log likelihood per observation) by the trained models. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running the experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. The paper only mentions "music21" and refers to "Python" without providing version information for these or other libraries. |
| Experiment Setup | Yes | For all models, all concentration and noise precision parameters are given Gamma(0.1, 0.1) priors. For the Sticky models, the ratio κ α+κ is given a Unif(0, 1) prior. We ran 5 Gibbs chains for 10,000 iterations each using the HDP-HMM-LT, Sticky-HDP-HMM-LT, HDP-HMM and Sticky-HDP-HMM models on the 200 training chorales... |