Spiking-LEAF: A Learnable Auditory Front-End for Spiking Neural Networks

Core Concepts

Spiking-LEAF enhances speech processing with a learnable auditory front-end for spiking neural networks.

Abstract

The content introduces the Spiking-LEAF model, designed to improve speech processing in spiking neural networks. It combines a learnable filter bank with a novel two-compartment spiking neuron model called IHC-LIF. The model outperforms existing auditory front-ends in keyword spotting and speaker identification tasks, showcasing higher accuracy, noise robustness, and encoding efficiency. The paper details the architecture, methods used, experimental results, ablation studies, and conclusions. Abstract: Introduces Spiking-LEAF for SNN-based speech processing. Combines learnable filter bank with IHC-LIF neuron model. Introduction: SNNs excel in sequential modeling but lag in speech tasks. Methods: Features Gabor 1d-convo filter bank and PCEN for extraction. Results: Spiking-LEAF surpasses existing front-ends on KWS and SI tasks. Ablation Studies: Learnable features enhance representation power. Conclusion: Spiking-LEAF offers improved feature extraction and encoding efficiency.

Stats

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Quotes

"Spiking LEarnable Audio front-end model, called Spiking-LEAF." - Content "Our proposed Spiking LEarnable Audio front-end shows high classification accuracy." - Content

Key Insights Distilled From

Spiking-LEAF

by Zeyang Song,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2309.09469.pdf

Deeper Inquiries

How can the lateral feedback mechanism enhance frequency sensitivity?

The lateral feedback mechanism enhances frequency sensitivity by modulating neighboring frequency bands through weight matrices. This modulation helps in adjusting the frequency sensitivity of auditory neurons, similar to how it occurs in the peripheral auditory system. By incorporating lateral feedback components into both dendritic and somatic compartments of neurons, as seen in the IHC-LIF model, unwanted spikes can be filtered out effectively. This process results in a more refined and precise spike representation that captures essential information while suppressing noise or irrelevant signals. Overall, the lateral feedback mechanism contributes to improving the neural encoding process by enhancing frequency selectivity and optimizing signal processing for tasks like speech recognition.

What are the implications of elevated firing rates on encoding efficiency?

Elevated firing rates have significant implications on encoding efficiency within spiking neural networks (SNNs). When firing rates are high, it often leads to increased energy consumption due to frequent spikes being generated per neuron per timestep. This heightened activity can result in inefficiencies during information processing as excessive spikes may encode redundant or noisy information rather than relevant features from input signals. Moreover, elevated firing rates could lead to issues such as spike collisions or overlapping spikes that might distort temporal information representation within SNNs. In terms of computational resources and hardware constraints, high firing rates can strain neuromorphic platforms with limited capabilities for real-time processing tasks. Therefore, maintaining an optimal balance between firing rates and spike generation is crucial for ensuring efficient neural encoding processes within SNNs.

How does the Spiking LEarnable Audio front-end impact edge computing?

The Spiking LEarnable Audio front-end has a profound impact on edge computing by enabling efficient speech processing at the edge using neuromorphic solutions. By integrating a learnable filter bank with novel neuron models like IHC-LIF into its architecture, this front-end optimizes feature extraction and neural encoding processes simultaneously for tasks such as keyword spotting (KWS) and speaker identification (SI). One key advantage is its ability to achieve superior feature representation power compared to traditional methods like Mel-scaled filter banks or handcrafted acoustic features commonly used in non-spiking artificial neural networks (ANNs). The learnability aspect allows for adaptive optimization of feature extraction based on specific task requirements without manual tuning of hyperparameters. Moreover, by enhancing noise robustness through mechanisms like lateral feedback and incorporating spike rate regularization loss for improved efficiency during spike encoding, the Spiking LEarnable Audio front-end ensures reliable performance even in noisy environments typical at edge devices. Overall, this innovative approach not only boosts classification accuracy but also paves the way for ultra-low-power speech processing applications at resource-constrained edge devices where traditional ANNs may face limitations related to computational complexity and energy consumption.