# Weakly Supervised Detection and Temporal Localization of Whale Calls in Long-Duration Bioacoustic Data

arXiv: https://arxiv.org/abs/2502.20838 · HTML: https://mlsanity.com/paper/2502.20838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events](https://mlsanity.com/paper/1904.05204/) (2019)
- [Instance-level loss based multiple-instance learning framework for acoustic scene classification](https://mlsanity.com/paper/2203.08439/) (2022)

## Built on by (most cited first, 1 of 1)

- [Ecologically-Constrained Task Arithmetic for Multi-Taxa Bioacoustic Classifiers Without Shared Data](https://mlsanity.com/paper/2605.03914/) (2026)

## Abstract (arXiv)

Passive acoustic monitoring (PAM) systems generate continuous recordings spanning months, yet automated bioacoustic analysis of whale calls requires two separate annotation efforts: binary presence labels for classification and precise temporal boundaries for localization. A binary label for a multi-minute recording can be assigned in seconds, but timestamping every call within it requires hours of expert effort. Providing both is infeasible at operational scale. We present DSMIL-LocNet, a weakly supervised multiple instance learning (MIL) framework that performs both classification and temporal localization using only recording-level presence/absence labels. Our dual-stream architecture integrates spectral and temporal features to process recordings of 2--30 minutes without the temporal compression that degrades existing CNN methods on long inputs. On the AcousticTrends BlueFinLibrary, DSMIL-LocNet achieves F1 scores of 0.88--0.91 on recordings of 300--1800s, where fully supervised CNN baselines degrade to 0.19--0.64. It also provides temporal localization that these baselines cannot produce without frame-level annotation. Code: https://github.com/Ragib-Amin-Nihal/DSMIL-Loc