Abstract
Despite the advancements in cutting-edge technologies, audio signal processing continues to pose challenges and lacks the precision of a human speech processing system. To address these challenges, we propose a novel approach to simplify audio signal processing by leveraging time-domain techniques and reservoir computing. Through our research, we have developed a real-time audio signal processing system by simplifying audio signal processing through the utilization of reservoir computers, which are significantly easier to train. Feature extraction is a fundamental step in speech signal processing, with Mel Frequency Cepstral Coefficients (MFCCs) being a dominant choice due to their perceptual relevance to human hearing. However, conventional MFCC extraction relies on computationally intensive time-frequency transformations, limiting efficiency in real-time applications. To address this, we propose a novel approach that leverages reservoir computing to streamline MFCC extraction. By replacing traditional frequency-domain conversions with convolution operations, we eliminate the need for complex transformations while maintaining feature discriminability. We present an end-to-end audio processing framework that integrates this method, demonstrating its potential for efficient and real-time speech analysis. Our results contribute to the advancement of energy-efficient audio processing technologies, enabling seamless deployment in embedded systems and voice-driven applications. This work bridges the gap between biologically inspired feature extraction and modern neuromorphic computing, offering a scalable solution for next-generation speech recognition systems.
Abstract (translated)
尽管尖端技术不断进步，音频信号处理仍面临诸多挑战，且其精度远不及人类语音处理系统。为应对这些挑战，我们提出了一种利用时域技术和储备计算简化音频信号处理的新方法。通过本研究，我们借助更易训练的储备计算机简化了音频信号处理流程，成功构建了一套实时音频信号处理系统。特征提取是语音信号处理的基础步骤，梅尔频率倒谱系数（MFCCs）因其与人类听觉感知的高度相关性而成为主流选择。然而传统MFCC提取依赖计算密集的时频变换，限制了实时应用效率。为此，我们提出利用储备计算优化MFCC提取的新方案——通过用卷积运算替代传统频域转换，在保持特征判别力的同时消除了复杂变换需求。我们展示了集成该方法的端到端音频处理框架，验证了其在高效实时语音分析中的潜力。本研究结果推动了节能型音频处理技术的发展，为嵌入式系统及语音驱动应用的无缝部署提供了可能。这项工作弥合了生物启发特征提取与现代神经形态计算之间的鸿沟，为下一代语音识别系统提供了可扩展的解决方案。
URL
https://arxiv.org/abs/2603.24283
