AMAuT: A Flexible and Efficient Multiview Audio Transformer Framework Trained from Scratch
Abstract
Recent foundational models, SSAST, EAT, HuBERT, Qwen-Audio, and Audio Flamingo, achieve top-tier results across standard audio benchmarks but are limited by fixed input rates and durations, hindering their reusability. This paper introduces the Augmentation-driven Multiview Audio Transformer (AMAuT), a training-from-scratch framework that eliminates the dependency on pre-trained weights while supporting arbitrary sample rates and audio lengths. AMAuT integrates four key components: (1) augmentation-driven multiview learning for robustness, (2) a conv1 + conv7 + conv1 one-dimensional CNN bottleneck for stable temporal encoding, (3) dual CLS + TAL tokens for bidirectional context representation, and (4) test-time adaptation/augmentation (TTA^2) to improve inference reliability. Experiments on five public benchmarks, AudioMNIST, SpeechCommands V1 & V2, VocalSound, and CochlScene, show that AMAuT achieves accuracies up to 99.8% while consuming less than 3% of the GPU hours required by comparable pre-trained models. Thus, AMAuT presents a highly efficient and flexible alternative to large pre-trained models, making state-of-the-art audio classification accessible in computationally constrained settings.
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 main contribution of this paper is the introduction of AMAuT, a flexible and efficient multiview audio transformer framework that eliminates the dependency on pre-trained weights while achieving state-of-the-art performance on audio classification tasks. This work significantly advances the field by addressing the limitations of existing models and providing a practical solution for diverse audio processing scenarios.
Methodology
The proposed AMAuT framework introduces a novel architecture that integrates augmentation-driven multiview learning, a flexible 1D CNN bottleneck, and dual CLS + TAL tokens for improved contextual representation. The methodology is well-structured, allowing for arbitrary sample rates and audio lengths, which is a significant advancement over existing models that rely on fixed input constraints. The use of test-time adaptation and augmentation ($TTA^2$) enhances robustness, making the approach comprehensive and innovative.
Results & evaluation
The experiments conducted on five public benchmarks demonstrate that AMAuT achieves high accuracy (up to 99.8%) while significantly reducing computational costs compared to pre-trained models. The results are compelling, showcasing the framework's efficiency and flexibility across different datasets. However, the paper could benefit from more detailed comparisons with other state-of-the-art models beyond just accuracy metrics.
Reproducibility
The authors provide a GitHub repository for the AMAuT implementation, which supports reproducibility. However, the paper lacks detailed hyperparameter settings and training configurations, which are crucial for ensuring that other researchers can replicate the results accurately.
Limitations
The paper acknowledges limitations such as sensitivity to dataset size and hyperparameter dependence, particularly for smaller datasets. Additionally, the inference latency introduced by the test-time adaptation process could hinder real-time applications.
Broader impact
The AMAuT framework has the potential to democratize access to high-performance audio classification by enabling researchers and practitioners with limited computational resources to train state-of-the-art models from scratch. Its flexibility and efficiency could lead to broader applications in real-time audio processing, embedded systems, and low-resource environments. The main contribution of this paper is the introduction of AMAuT, a flexible and efficient multiview audio transformer framework that eliminates the dependency on pre-trained weights while achieving state-of-the-art performance on audio classification tasks. This work significantly advances the field by addressing the limitations of existing models and providing a practical solution for diverse audio processing scenarios.
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