Abstract
With the advent of deep learning, research on noise-robust sound event detection (SED) has progressed rapidly. However, SED performance in noisy conditions of single-channel systems remains unsatisfactory. Recently, there were several speech enhancement (SE) methods for the SED front-end to reduce the noise effect, which are completely two models that handle two tasks separately. In this work, we introduced a network trained by a two-stage method to simultaneously perform signal denoising and SED, where denoising and SED are conducted sequentially using neural network method. In addition, we designed a new objective function that takes into account the Euclidean distance between the output of the denoising block and the corresponding clean audio amplitude spectrum, which can better limit the distortion of the output features. The two-stage model is then jointly trained to optimize the proposed objective function. The results show that the proposed network presents a better performance compared with single-stage network without noise suppression. Compared with other recent state-of-the-art networks in the SED field, the performance of the proposed network model is competitive, especially in noisy environments.
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Ou, J., Liu, H., Zhou, Y., Gan, L. (2022). Robust Sound Event Detection by a Two-Stage Network in the Presence of Background Noise. In: Gao, H., Wun, J., Yin, J., Shen, F., Shen, Y., Yu, J. (eds) Communications and Networking. ChinaCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 433. Springer, Cham. https://doi.org/10.1007/978-3-030-99200-2_34
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