A generalized network based on multi-scale densely connection and residual attention for sound source localization and detection

Abstract

Sound source localization and detection (SSLD) is a joint task of identifying the presence of individual sound events and locating the sound sources in space. However, due to the diversity of sound events and the variability of sound source location, SSLD becomes a tough task. In this paper, we propose a SSLD method based on a multi-scale densely connection (MDC) mechanism and a residual attention (RA) mechanism. We design a MDC block to integrate the information from a very local to exponentially enlarged receptive field within the block. We also explored three kinds of RA blocks that can facilitate the conductivity of information flow among different layers by continuously adding feature maps from the previous layers to the next layer. In order to recalibrate the feature maps after convolutional operation, we design a dual-path attention (DPA) unit that is largely embodied in MDC and RA blocks. We firstly verified the effectiveness of the MDC block, RA block, and DPA unit, respectively. We then compared our proposed method with another four methods on the development dataset; finally, with SELDnet and SELD-TCN on another five datasets, experimental results show the generalization of our proposed method.

GLFER-Net: a polyphonic sound source localization and detection network based on global-local feature extraction and recalibration

“…Various sound events exhibit the diversity of duration and frequency distribution range [27]. Designing an effective feature extractor is necessary to obtain richer spatial information for the SSLD task.…”