Speaker: Sara Barahona Quirós.

Abstract: The Conformer architecture has achieved state-of-the-art results in several tasks, including automatic speech recognition and automatic speaker verification. However, its utilization in sound event detection and in particular in the DCASE Challenge Task 4 has been limited despite winning the 2020 edition. Although the Conformer architecture may not excel in accurately localizing sound events, it shows promising potential in minimizing confusion between different classes. Therefore, in this paper we propose a Conformer optimization to enhance the second Polyphonic Sound Detection Score (PSDS) scenario defined for the DCASE 2023 Task 4A. With the aim of maximizing its classification properties, we have employed recently proposed methods such as Frequency Dynamic Convolutions in addition to our multi-resolution approach, which allow us to analyze its behavior over different time-frequency resolution points. Furthermore, our Conformer systems are compared with multi-resolution models based on Convolutional Recurrent Neural Networks (CRNNs) to evaluate the respective benefits of each architecture in relation to the two proposed scenarios for the PSDS and the different time-frequency resolution points defined. These systems were submitted as our participation in the DCASE 2023 Task 4A, in which our Conformer system obtained a PSDS2 value of 0.728, achieving one of the highest scores for this scenario among systems trained without external resources.