Sound Event Detection and Direction of Arrival Estimation using Residual Net and Recurrent Neural Networks

Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019

“…Essentially, their work disassociates the joint cost function combining SED losses and localization losses as realized in the baseline and trains individual models for each task. The SED and DoA estimates are then associated through a training strategy or assigned randomly between them [23], [25], [26]. It has to be noted that such random association takes advantage of the fact that detection and localization were evaluated independently in the challenge and would not be a good strategy in practice.…”

“…It has to be noted that such random association takes advantage of the fact that detection and localization were evaluated independently in the challenge and would not be a good strategy in practice. Ranjan et al [26] compared the two-stage architecture versus joint-modeling, with clearly improved results with the former. However, it is worth noting that two systems in the top ten places had a single network performing joint-modeling [20], [27], one of them being third best [20].…”

“…Certain architectural or training choices were specific to the localization task. Some of the submissions treated DoA estimation as a classification task [25], [26], e.g. similar to other DNN-based localization works [14]- [16], instead of the regression format of the baseline.…”

“…No information was provided on how or if DoAs were associated with events and from further analysis on the following sections we assume that such association was done randomly, as in [23]. The same approach of independent SED and localization networks, a classification-based DoA estimation, and random association between the two was followed by the next best performing system of Ranjan et al (Ranjan NTU) [26]. Additionally, the authors replaced CNN layers with Resnets in the typical CRNN networks followed by most participants.…”

“…In general, it can be observed that submissions which employed separate localization and detection systems and did not handle association of the two properly were likely to slip in their ranks. This is especially evident on the systems that assigned randomly DoAs to detections, such as Nguyen NTU [23] and Ranjan NTU [26], including the best localization method of Chang HYU [25]. Their association problems are revealed both by their large drop in detection scores (F 10 • , ER 10 • ) and by the large error increase between their original LE and the class-dependent one LE CD .…”