Bridging Biological Hearing and Neuromorphic Computing: End-to-End Time-Domain Audio Signal Processing with Reservoir Computing

2026-03-25

AI Summary

This work proposes an end-to-end time-domain audio processing framework based on reservoir computing, addressing the limitations of traditional methods that rely on computationally intensive time–frequency transforms such as MFCCs and struggle to balance real-time performance, energy efficiency, and alignment with the human auditory system’s efficacy. By integrating biologically inspired auditory feature extraction with reservoir computing and replacing conventional frequency-domain transformations with lightweight convolutional operations, the proposed approach significantly reduces computational overhead while preserving discriminative feature representation. It eliminates the need for complex preprocessing and enables efficient, low-power real-time speech analysis, making it well-suited for embedded systems and voice-driven applications. This study thus establishes a highly energy-efficient and deployable paradigm for neuromorphic audio processing.

Technology Category

• Natural Language Processing: Speech
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• audio signal processing
• real-time
• MFCC
• time-frequency transformation
• feature extraction

Innovation

• Reservoir Computing
• Time-Domain Audio Processing
• MFCC Simplification
• Neuromorphic Computing
• End-to-End Speech Analysis