用于资源受限系统中鲁棒且高效的关键词检测的领域增量学习
Domain-Incremental Continual Learning for Robust and Efficient Keyword Spotting in Resource Constrained Systems
AI总结：
本文提出一种领域增量学习框架，通过高效去噪与原型连续学习结合，提升资源受限环境下关键词检测的鲁棒性和效率。
摘要：
在边缘设备上部署的小型足迹关键词检测（KWS）系统面临由于噪声和录音条件变化引起的领域偏移带来的显著准确性和鲁棒性挑战。为了解决这一问题，我们提出了一种连续学习框架，旨在适应新领域的同时保持计算效率。所提出的流程集成了双输入卷积神经网络，利用梅尔频率倒谱系数（MFCC）和梅尔频谱图特征，辅以多阶段去噪过程，包括离散小波变换和频谱减法技术，以及模型和原型更新模块。与以往方法限制更新到特定层不同，我们的方法更新完整的量化模型，这得益于紧凑的模型架构。在运行时，通过类原型和基于置信度的过滤选择一部分输入样本，然后进行伪标签并结合再训练缓冲区进行增量模型再训练。在噪声测试数据集上的实验结果表明，该框架的有效性，实现了在干净数据上的99.63%准确率，并在多样化的噪声环境中保持鲁棒性能（超过94%的准确率），即使在-10 dB信噪比下。所提出的框架表明，将高效的去噪与基于原型的连续学习相结合，使KWS模型能够在资源受限、动态环境中自主且鲁棒地运行。
英文摘要：
Keyword Spotting (KWS) systems with small footprint models deployed on edge devices face significant accuracy and robustness challenges due to domain shifts caused by varying noise and recording conditions. To address this, we propose a comprehensive framework for continual learning designed to adapt to new domains while maintaining computational efficiency. The proposed pipeline integrates a dual-input Convolutional Neural Network, utilizing both Mel Frequency Cepstral Coefficients (MFCC) and Mel-spectrogram features, supported by a multi-stage denoising process, involving discrete wavelet transform and spectral subtraction techniques, plus model and prototype update blocks. Unlike prior methods that restrict updates to specific layers, our approach updates the complete quantized model, made possible due to compact model architecture. A subset of input samples are selected during runtime using class prototypes and confidence-driven filtering, which are then pseudo-labeled and combined with rehearsal buffer for incremental model retraining. Experimental results on noisy test dataset demonstrate the framework's effectiveness, achieving 99.63\% accuracy on clean data and maintaining robust performance (exceeding 94\% accuracy) across diverse noisy environments, even at -10 dB Signal-to-Noise Ratio. The proposed framework work confirms that integrating efficient denoising with prototype-based continual learning enables KWS models to operate autonomously and robustly in resource-constrained, dynamic environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
