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.

翻译：尽管前沿技术不断进步，音频信号处理仍然面临挑战，且难以达到人类语音处理系统的精度。为应对这些挑战，我们提出了一种新颖方法，通过利用时域技术和储层计算来简化音频信号处理。通过研究，我们开发了一个实时音频信号处理系统，该系统通过使用训练难度显著降低的储层计算机来简化音频信号处理过程。特征提取是语音信号处理中的基本步骤，其中梅尔频率倒谱系数（MFCCs）因其与人耳听觉感知的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，限制了实时应用中的效率。为此，我们提出了一种新颖方法，利用储层计算来简化MFCC提取流程。通过用卷积运算替代传统频域转换，我们在保持特征判别能力的同时，消除了复杂变换的需求。我们呈现了一个集成该方法的端到端音频处理框架，展示了其在高效实时语音分析中的潜力。我们的研究成果促进了高能效音频处理技术的发展，使其能够无缝部署于嵌入式系统和语音驱动应用中。这项工作架起了生物启发特征提取与现代神经形态计算之间的桥梁，为下一代语音识别系统提供了可扩展的解决方案。