A Human-Inspired Decoupled Architecture for Efficient Audio Representation Learning
Abstract
While self-supervised learning (SSL) has revolutionized audio representation, the excessive parameterization and quadratic computational cost of standard Transformers limit their deployment on resource-constrained devices. To address this bottleneck, we propose HEAR (Human-inspired Efficient Audio Representation), a novel decoupled architecture. Inspired by the human cognitive ability to isolate local acoustic features from global context, HEAR splits the processing pipeline into two dedicated modules: an Acoustic Model for local feature extraction and a Task Model for global semantic integration. Coupled with an Acoustic Tokenizer trained via knowledge distillation, our approach enables robust Masked Audio Modeling (MAM). Extensive experiments demonstrate that HEAR requires only 15M parameters and 9.47 GFLOPs for inference, operating at a fraction of the computational cost of conventional foundation models (which typically require 85M-94M parameters). Despite this high efficiency, HEAR achieves highly competitive performance across diverse audio classification benchmarks. The code and pre-trained models are available at https://github.com/HarunoriKawano/HEAR
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents HEAR, a human-inspired audio representation framework that effectively decouples local feature extraction from global task adaptation, achieving high efficiency and competitive performance in audio classification tasks. This innovative approach addresses critical computational challenges in deploying self-supervised learning models in resource-constrained environments, marking a significant advancement in the field of audio representation learning.
Methodology
The proposed HEAR framework introduces a decoupled architecture inspired by human auditory processing, effectively separating local feature extraction and global task adaptation. This innovative approach addresses the computational inefficiencies of standard Transformer architectures by utilizing an Acoustic Model and a Task Model, which enhances the model's efficiency while maintaining competitive performance across various audio classification tasks. The use of knowledge distillation for an Acoustic Tokenizer further strengthens the model's ability to learn robust audio representations.
Results & evaluation
The experiments conducted are extensive, utilizing a large-scale dataset of over 10,000 hours of audio for pre-training. The paper evaluates the model's performance on multiple benchmark datasets, demonstrating its competitive accuracy despite significantly reduced parameters and computational costs. The ablation studies provide insights into the contributions of various components of the architecture, validating the effectiveness of the proposed methods.
Reproducibility
The paper includes detailed descriptions of the architecture, training procedures, and hyperparameter settings, which should facilitate reproducibility. The availability of code and pre-trained models on GitHub further supports this aspect, although the lack of a demo URL may limit immediate accessibility for practical applications.
Limitations
While the HEAR architecture shows promise, its performance on certain tasks is still lower compared to larger models, indicating a trade-off between efficiency and accuracy. Additionally, the reliance on specific datasets for training and evaluation may limit the generalizability of the findings to other audio tasks.
Broader impact
The HEAR framework has significant implications for real-time audio processing on resource-constrained devices, making it suitable for applications in mobile devices, IoT, and edge computing. Its efficiency could enable broader adoption of advanced audio representation techniques in practical applications, such as speech recognition and environmental sound classification. The paper presents HEAR, a human-inspired audio representation framework that effectively decouples local feature extraction from global task adaptation, achieving high efficiency and competitive performance in audio classification tasks. This innovative approach addresses critical computational challenges in deploying self-supervised learning models in resource-constrained environments, marking a significant advancement in the field of audio representation learning.
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