Wataru Kohno NEC Labs America

Wataru Kohno

Researcher

Optical Networking & Sensing

Posts

CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic Recognition

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Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber-optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiberoptic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory recorded fiber-optic ESC-50 datasets and a real-world fiber optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks.

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Text-guided Device-realistic Sound Generation for Fiber-based Sound Event Classification

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Recent advancements in unique acoustic sensing devices and large-scale audio recognition models have unlocked new possibilities for environmental sound monitoring and detection. However, applying pretrained models to non-conventional acoustic sensors results in performance degradation due to domain shifts, caused by differences in frequency response and noise characteristics from the original training data. In this study, we introduce a text-guided framework for generating new datasets to retrain models specifically for these non-conventional sensors efficiently. Our approach integrates text-conditional audio generative models with two additional steps: (1) selecting audio samples based on text input to match the desired sounds, and (2) applying domain transfer techniques using recorded impulse responses and background noise to simulate the characteristics of the sensors. We demonstrate this process by generating emulated signals for fiber-optic Distributed Acoustic Sensors (DAS), creating datasets similar to the recorded ESC-50 dataset. The generated signals are then used to train a classifier, which outperforms few-shot learning approaches in environmental sound classification.

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Trainingless Adaptation of Pretrained Models for Environmental Sound Classification

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Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or unseen data. To utilize pretrained models for the unseen domain, adaptation methods, such as finetuning and transfer learning, are used with rich computing resources, e.g., the graphical processing unit (GPU). However, it is becoming more difficult to keep up with research trends for those who have poor computing resources because state-of-the-art models are becoming computationally resource-intensive. In this paper, we propose a trainingless adaptation method for pretrained models for environmental sound classification. To introduce the trainingless adaptation method, we first propose an operation of recovering time–frequency-ish (TF-ish) structures in intermediate layers of DNN models. We then propose the trainingless frequency filtering method for domain adaptation, which is not a gradient-based optimization widely used. The experiments conducted using the ESC-50 dataset show that the proposed adaptation method improves the classification accuracy by 20.40 percentage points compared with the conventional method.

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Multi-Event Distributed Forwarding Sensing with Dual-Sensor Adaptive Beamforming

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We present adaptive beamforming techniques to forward-transmission multi-event vibration sensing in environments with interference and jamming. Experimental validation over 100km fiber demonstrates significant improvements on signal reconstruction, noise reduction, and interference rejection from other locations.

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Underwater Acoustic OFDM Transmission over Optical Fiber with Distributed Acoustic Sensing

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We demonstrate fiber-optic acoustic data transmission using distributed acoustic sensing technology in an underwater environment. An acoustic orthogonal frequencydivisionmultiplexing (OFDM) signal transmitted through a fiber-optic cable deployed in a standard 40-meter-scale underwater testbed.

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CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic Recognition

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Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiber-optic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory-recorded fiber-optic ESC-50 datasets and a real-world fiber-optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks.

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Remote Sensing for Power Grid Fuse Tripping Using AI-Based Fiber Sensing with Aerial Telecom Cables

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For the first time, we demonstrate remote sensing of pole-mounted fuse-cutout blowing in a power grid setup using telecom fiber cable. The proposed frequency-based AI model achieves over 98% detection accuracy using distributed fiber sensing data.

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Low-rank Constrained Multichannel Signal Denoising Considering Channel-dependent Sensitivity Inspired by Self-supervised Learning for Optical Fiber Sensing

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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.

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Distributed Fiber-Optic Sensor as an Acoustic Communication Receiver Array

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A novel acoustic transmission technique using distributed acoustic sensors is introduced. By choosing better incident angles for smaller fading and employing an 8- channel beamformer, over 10KB data is transmitted at a 6.4kbps data rate.

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OFDM Signal Transmission Using Distributed Fiber-Optic Acoustic Sensing

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Acoustic data transmission with the Orthogonal Frequency Division Multiplexing (OFDM) signal has been demonstrated using a Distributed Acoustic Sensor (DAS) based on Phase-sensitive Optical Time-Domain Reflectometry (?-OTDR).

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