Soundbay: Deep Learning Framework for Marine Mammals and Bioacoustic Research

This paper presents Soundbay, an open-source Python framework that allows bio-acoustics and machine learning researchers to implement and utilize deep learning-based algorithms for acoustic audio analysis. Soundbay provides an easy and intuitive platform for applying existing models on one's data or creating new models effortlessly. One of the main advantages of the framework is the capability to compare baselines on different benchmarks, a crucial part of emerging research and development related to the usage of deep-learning algorithms for animal call analysis. We demonstrate this by providing a benchmark for cetacean call detection on multiple datasets. The framework is publicly accessible via https://github.com/deep-voice/soundbay

Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations

Transformer networks have been a focus of research in many fields in recent years, being able to surpass the state-of-the-art performance in different computer vision tasks. A few attempts have been made to apply this method to the task of Multiple Object Tracking (MOT), among those the state-of-the-art was TransCenter, a transformer-based MOT architecture with dense object queries for accurately tracking all the objects while keeping reasonable runtime. TransCenter is the first center-based transformer framework for MOT, and is also among the first to show the benefits of using transformer-based architectures for MOT. In this paper we show an improvement to this tracker using post processing mechanism based in the Track-by-Detection paradigm: motion model estimation using Kalman filter and target Re-identification using an embedding network. Our new tracker shows significant improvements in the IDF1 and HOTA metrics and comparable results on the MOTA metric (70.9%, 59.8% and 75.8% respectively) on the MOTChallenge MOT17 test dataset and improvement on all 3 metrics (67.5%, 56.3% and 73.0%) on the MOT20 test dataset. Our tracker is currently ranked first among transformer-based trackers in these datasets. The code is publicly available at: https://github.com/amitgalor18/STC_Tracker