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..
Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity
Authors: Forrest Briggs, Xiaoli Fern, Raviv Raich, Matthew Betts
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a comparative study, we show that the proposed methods discover more species/classes than current state-of-the-art in a real world dataset of 92,095 ten-second recordings collected in field conditions. We apply our proposed methods, and baseline methods, to a real-world dataset of 92,095 ten-second recordings, collected at 13 sites over a period of two months, in a research forest. These recordings pose many challenges for automatic species discovery, including multiple simultaneously vocalizing birds of different species, non-bird sounds such as motor sound, and environmental noises, e.g., wind, rain, streams, and thunder. Our results show that the proposed methods discover more species/classes than previous methods. The results of the experiment are viewed in terms of a graph of number of species or classes discovered vs. number of recordings labeled. |
| Researcher Affiliation | Collaboration | Forrest Briggs Facebook, Inc. EMAIL Xiaoli Z. Fern, Raviv Raich, Matthew Betts Oregon State University EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-Instance Farthest First (MIFF) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | No | In this study, we collected audio data at 13 different sites in the H. J. Andrews Long Term Experimental Research Forest over a two-month period during the 2009 breeding season. we divided the full dataset into 920,956 ten-second intervals, then randomly subsampled 10% of this data, to obtain a total of 92,095 tens-second recordings for our experiments. We annotated 150 randomly chosen ten-second recording spectrograms as examples for segmentation. Figure 1 shows an example of an annotated spectrogram for training the segmentation algorithm. A further 1000 randomly chosen recordings are labeled as rain or non-rain to train the rain filter. However, there is no explicit link or statement about this dataset being publicly available. |
| Dataset Splits | No | From the pool of 92,095 recordings, we apply each of the methods (dawn, cluster centers, MIFF, CCMIFF) to select m = 100 recordings to be labeled. This 100 recordings are the target of their 'discovery'. There is no explicit training, validation, or test split for the *discovery* task, as the task is to select from an unlabeled pool. For the rain filter, it says "trained on 1000 ten-second recordings" but doesn't mention validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms like 'k-means++' and 'random forest classifier' but does not specify any software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | For MIFF and CCMIFF, we set the parameter p = 2 because we expect on average to have 2 classes per bag. For CCMIFF, we set the number of clusters k = 1000, based on the observation that with a smaller number of clusters (e.g., 100), the algorithm covers all clusters very early on, before selecting m = 100 bags. We compare the species discovered by cluster centers, MIFF, and CCMIFF with rain filter threshold T {0.1, 0.01} or no rain filter. |