Sound event detection (SED) recognizes the corresponding sound event of an incoming signal and estimates its temporal boundary. Although SED has been recently developed and used in various fields, achieving noise-robust SED in a real environment is typically challenging owing to the performance degradation due to ambient noise. In this paper, we propose combining a pretrained time-domain speech-separation-based noise suppression network (NS) and a pretrained classification network to improve the SED performance in real noisy environments. We use group communication with a context codec method (GC3)-equipped temporal convolutional network (TCN) for the noise suppression model and a convolutional recurrent neural network for the SED model. The former significantly reduce the model complexity while maintaining the same TCN module and performance as a fully convolutional time-domain audio separation network (Conv-TasNet). We also do not update the weights of some layers (i.e., freeze) in the joint fine-tuning process and add an attention module in the SED model to further improve the performance and prevent overfitting. We evaluate our proposed method using both simulation and real recorded datasets. The experimental results show that our method improves the classification performance in a noisy environment under various signal-to-noise-ratio conditions.
In this paper, a multi-input multi-output (MIMO) noise suppression (NS) algorithm for sound source localization (SSL) is proposed in mobile robot environment. Especially for the mobile robot, some ego noise (e.g., motor noise) is naturally generated and it becomes a dominant signal at microphone array, which seriously damages the performance of the SSL. Therefore, the proposed MIMO NS algorithm is designed not only to suppress the noise but also to preserve spatial information of sound sources. To this end, the recent MIMO time-domain audio separation network (TasNet) with preserving spatial cues for binaural source separation is extended to tetrahedral microphone array in the proposed MIMO NS algorithm. Furthermore, weighted phase error (WPE) between inter-channels is additionally employed in the loss function, which can improve the performance especially in preserving inter-channel time difference (ITD). The performance of the proposed approach is verified within the context of the robot vacuum cleaner, which shows from simulations and real experiments that the proposed approach can preserve the spatial information as well as remove the vacuum noise.
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