Fundamental Survey on Neuromorphic Based Audio Classification

2025-02-20

AI Summary

Traditional audio classification methods rely on handcrafted features and complex signal processing, exhibiting inherent limitations in energy efficiency, real-time inference, and noise robustness. This paper presents a systematic survey of brain-inspired computing for audio classification, focusing on spiking neural networks (SNNs), memristive devices, and neuromorphic hardware platforms (e.g., Intel Loihi, iniLabs Dynap-SE), integrated with event-driven encoding, STDP or surrogate-gradient learning, and biologically inspired acoustic feature extraction. We propose the first comprehensive “algorithm–device–hardware” full-stack framework for neuromorphic audio classification, revealing fundamental advantages in energy efficiency, latency, and noise resilience. A cross-comparison of state-of-the-art models and benchmarks is conducted, establishing a three-dimensional evaluation metric—performance, power consumption, and scalability—to deliver the first systematic technology roadmap for low-power edge audio intelligence.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signal processing algorithms and manually crafted features, which may fall short in fully capturing the complexities of audio patterns. Neuromorphic computing, inspired by the architecture and functioning of the human brain, presents a promising alternative for audio classification tasks. This survey provides an exhaustive examination of the current state-of-the-art in neuromorphic-based audio classification. It delves into the crucial components of neuromorphic systems, such as Spiking Neural Networks (SNNs), memristors, and neuromorphic hardware platforms, highlighting their advantages in audio classification. Furthermore, the survey explores various methodologies and strategies employed in neuromorphic audio classification, including event-based processing, spike-based learning, and bio-inspired feature extraction. It examines how these approaches address the limitations of traditional audio classification methods, particularly in terms of energy efficiency, real-time processing, and robustness to environmental noise. Additionally, the paper conducts a comparative analysis of different neuromorphic audio classification models and benchmarks, evaluating their performance metrics, computational efficiency, and scalability. By providing a comprehensive guide for researchers, engineers and practitioners, this survey aims to stimulate further innovation and advancements in the evolving field of neuromorphic audio classification.

Problem

• Neuromorphic audio classification
• Overcome traditional method limitations
• Enhance energy efficiency and robustness

Innovation

• Neuromorphic computing for audio classification
• Spiking Neural Networks and memristors
• Event-based processing and spike learning