Low-rank constrained multichannel signal denoising considering channel-dependent sensitivity inspired by self-supervised learning for optical fiber sensing

Optical fiber sensing is a technology wherein audio, vibrations, and temperature are detected using an optical fiber; especially the audio/vibrations-aware sensing is called distributed acoustic sensing (DAS). In DAS, observed data, which is comprised of multichannel data, has suffered from severe noise levels because of the optical noise or the installation methods. In conventional methods for denoising DAS data, signal-processing- or deep-neural-network (DNN)-based models have been studied. The signal-processing-based methods have the interpretability, i.e., non-black box. The DNN-based methods are good at flexibility designing network architectures and objective functions, that is, priors. However, there is no balance between the interpretability and the flexibility of priors in the DAS studies. The DNN-based methods also require a large amount of training data in general. To address the problems, we propose a DNN-structure signal-processing-based denoising method in this paper. As the priors of DAS, we employ spatial knowledge; low rank and channel-dependent sensitivity using the DNN-based structure. The result of fiber-acoustic sensing shows that the proposed method outperforms the conventional methods and the robustness to the number of the spatial ranks. Moreover, the optimized parameters of the proposed method indicate the relationship with the channel sensitivity; the interpretability.

Sound Event Triage: Detecting Sound Events Considering Priority of Classes

We propose a new task for sound event detection (SED): sound event triage (SET). The goal of SET is to detect a high-priority event while allowing misdetections of low-priority events where the extent of priority is given for each event class. In conventional methods of SED for targeting a specific sound event class, only information on types of target sound can be treated. To flexibly control more wealth of information on the target event, the proposed SET exploits not only types of target sound but also the extent to which each target sound is detected with priority. To implement SET, we apply a method that allows the system input of the priority of sound events to be detected, which is based on the class-level loss-conditional training. Results of the experiment using the URBAN--SED dataset reveal that our SET scheme achieves reasonable detection performance in terms of frame-based and intersection-based F-scores. In particular, the proposed method of SET outperforms the conventional SED method by around 10 percentage points for some events.