Outlier-Robust Convex Segmentation
Authors: Itamar Katz, Koby Crammer
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Robustness to outliers is evaluated on two real-world tasks related to speech segmentation. Our algorithms outperform baseline segmentation algorithms. We evaluate the performance of the proposed algorithms on two speech segmentation tasks, for both clean sources and sources contaminated with added non-stationary noise. Our algorithms outperform other algorithms in both the clean and outlier-contaminated setting. |
| Researcher Affiliation | Academia | Itamar Katz and Koby Crammer Department of Electrical Engineering The Technion Israel Institute of Technology Haifa, 32000 Israel |
| Pseudocode | Yes | Algorithm 1 Top-down outlier-robust hierarchical segmentation |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or include links to a code repository. |
| Open Datasets | Yes | In this experiment we used the TIMIT corpus (Garofolo and others 1988). |
| Dataset Splits | No | The paper mentions using datasets like TIMIT and a hand-annotated audio recording, but it does not specify explicit training, validation, and test splits (e.g., percentages or sample counts) for reproduction. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory, or cloud platforms) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their versions) that would be needed for replication. |
| Experiment Setup | Yes | Audio was divided into frames of 16ms duration and 1 ms hop-length, each represented with 13 MFCC coefficients. We used 13 MFCC coefficient with 25ms window length and 10ms hop length. Then a Gaussian Mixture Model (GMM) Tj with 10 components and a diagonal covariance matrix is fitted to the jth block Sj. These parameters of the GMM were selected using the Bayesian Information Criterion (BIC). |