Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks (2306.04186v2)

Abstract: Self-supervised learning (SSL) has emerged as a popular approach for learning audio representations. One goal of audio self-supervised pre-training is to transfer knowledge to downstream audio tasks, generally including clip-level and frame-level tasks. While frame-level tasks are important for fine-grained acoustic scene/event understanding, prior studies primarily evaluate on clip-level downstream tasks. In order to tackle both clip-level and frame-level tasks, this paper proposes Audio Teacher-Student Transformer (ATST), with a clip-level version (named ATST-Clip) and a frame-level version (named ATST-Frame), responsible for learning clip-level and frame-level representations, respectively. Both methods use a Transformer encoder and a teacher-student training scheme. We have carefully designed the view creation strategy for ATST-Clip and ATST-Frame. Specifically, ATST-Clip uses segment-wise data augmentations, and ATST-Frame integrates frame-wise data augmentations and masking. Experimental results show that our ATST-Frame model obtains state-of-the-art (SOTA) performances on most of the clip-level and frame-level downstream tasks. Especially, it outperforms other models by a large margin on the frame-level sound event detection task. In addition, the performance can be further improved by combining the two models through knowledge distillation. Our code is available online.

• The paper presents the ATST framework, which utilizes a teacher-student paradigm to address both clip-level and frame-level audio tasks.
• It details two model variants—ATST-Clip and ATST-Frame—that use innovative data augmentation and masking strategies to enhance representation learning.
• Empirical results demonstrate state-of-the-art performance in sound event detection, with knowledge distillation further boosting overall task accuracy.

An Expert Review of "Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks"

The paper entitled "Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks" introduces an innovative approach to developing self-supervised learning (SSL) methodologies for audio representation learning, with the aim to effectively address both clip-level and frame-level tasks. The authors propose a novel architecture, the Audio Teacher-Student Transformer (ATST), and evaluate its performance compared to state-of-the-art techniques.

At its core, the paper aims to overcome the prevailing limitation where most SSL methods for audio are predominantly assessed on clip-level tasks. The fine-grained acoustic scene understanding, which necessitates precise frame-level event detection, serves as a more complex yet crucial objective, particularly for tasks such as sound event detection and speaker diarization.

Methodological Contributions

The primary contribution of the paper is the development of the ATST framework, which consists of two variants: ATST-Clip for clip-level tasks and ATST-Frame for frame-level tasks. Both models utilize a Transformer encoder combined with a teacher-student training paradigm.

• ATST-Clip: Focused on gleaning a global representation of audio clips, ATST-Clip employs a segment-wise data augmentation strategy. A distinguishing feature of this approach is its emphasis on creating two augmented views of an audio clip, facilitating a robust pre-training process that enhances the model's ability to capture semantic audio features.
• ATST-Frame: To explicitly capture frame-wise representations, ATST-Frame incorporates frame-wise data augmentation alongside strategic masking of the input sequence. This design encourages the model to learn semantic correlations across frames, which is key to improving frame-level task performance, such as sound event detection.

Empirical Evaluations

The experimental results are compelling, with ATST-Frame achieving state-of-the-art performance on numerous downstream tasks. Particularly noteworthy is its exemplary performance on frame-level sound event detection, surpassing other models by significant margins. The authors further demonstrate that a knowledge distillation technique, whereby the two model variants are combined, can further amplify task performance.

Implications and Future Directions

The proposed approach not only delivers a substantial performance boost in audio-oriented tasks but also provides a robust architecture that could be extended to other domains requiring fine-grained temporal understanding. The versatility of the ATST framework underscores the potential benefits of a hybrid model that can capture both broad and detailed audio event representations.

In the context of AI developments, this work highlights the importance of leveraging self-supervised learning to reduce reliance on extensive labeled datasets, facilitating broader applications and new avenues of research in audio processing and related domains. As future work, exploring the transferability of this dual-focused learning approach to other modalities and cross-modal tasks could yield further advancements in self-supervised learning paradigms.