CoLoC: Conditioned Localizer and Classifier for Sound Event Localization and Detection
This addresses sound event localization and detection for audio processing applications, but appears incremental as it builds on existing SELDnet-like architectures.
The paper tackles sound event localization and detection by proposing CoLoC, a two-stage method where localization precedes classification conditioned on localizer output, using sequential set generation to handle unknown numbers of sources. It shows improvement over baseline systems on the STARSS22 Dataset in most metrics.
In this article, we describe Conditioned Localizer and Classifier (CoLoC) which is a novel solution for Sound Event Localization and Detection (SELD). The solution constitutes of two stages: the localization is done first and is followed by classification conditioned by the output of the localizer. In order to resolve the problem of the unknown number of sources we incorporate the idea borrowed from Sequential Set Generation (SSG). Models from both stages are SELDnet-like CRNNs, but with single outputs. Conducted reasoning shows that such two single-output models are fit for SELD task. We show that our solution improves on the baseline system in most metrics on the STARSS22 Dataset.