BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations (2204.07402v2)

Abstract: Pre-trained models are essential as feature extractors in modern machine learning systems in various domains. In this study, we hypothesize that representations effective for general audio tasks should provide multiple aspects of robust features of the input sound. For recognizing sounds regardless of perturbations such as varying pitch or timbre, features should be robust to these perturbations. For serving the diverse needs of tasks such as recognition of emotions or music genres, representations should provide multiple aspects of information, such as local and global features. To implement our principle, we propose a self-supervised learning method: Bootstrap Your Own Latent (BYOL) for Audio (BYOL-A, pronounced "viola"). BYOL-A pre-trains representations of the input sound invariant to audio data augmentations, which makes the learned representations robust to the perturbations of sounds. Whereas the BYOL-A encoder combines local and global features and calculates their statistics to make the representation provide multi-aspect information. As a result, the learned representations should provide robust and multi-aspect information to serve various needs of diverse tasks. We evaluated the general audio task performance of BYOL-A compared to previous state-of-the-art methods, and BYOL-A demonstrated generalizability with the best average result of 72.4% and the best VoxCeleb1 result of 57.6%. Extensive ablation experiments revealed that the BYOL-A encoder architecture contributes to most performance, and the final critical portion resorts to the BYOL framework and BYOL-A augmentations. Our code is available online at https://github.com/nttcslab/byol-a for future studies.

• The paper introduces a novel self-supervised learning approach by adapting BYOL to generate robust audio representations without task-specific fine-tuning.
• It employs innovative data augmentations like Mixup, Random Resize Crop, and Random Linear Fader to capture diverse audio variations.
• Experimental evaluations on tasks such as sound event recognition and music analysis demonstrate that BYOL-A matches or exceeds state-of-the-art performance.

Overview of "BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations"

The paper "BYOL for Audio" tackles the task of developing pre-trained general-purpose audio representations using a self-supervised learning approach, specifically modifying the Bootstrap Your Own Latent (BYOL) framework for audio data. The authors present BYOL for Audio (BYOL-A) as a novel self-supervised learning method for pre-training audio representations that achieve robust performance across a diverse range of audio tasks without the necessity of extra fine-tuning. BYOL-A aims to create representations that are invariant to perturbations such as pitch, time, and amplitude variations, enabling them to be useful in tasks ranging from sound event recognition to music analysis and emotional classification.

Key Contributions and Methodology

The core proposition of BYOL-A is to derive robust audio representations that can accommodate varying needs across different audio-related tasks by synthesizing multi-aspect information from audio inputs. The framework extends the BYOL method to the audio domain through a series of carefully designed augmentations:

1. Mixup for Background Sound Perturbation: Alters the audio by mixing it with other audio samples, which simulates a variety of real-world environments.
2. Random Resize Crop (RRC): Applied on spectrograms to approximate shifts and stretches along frequency and time axes, crafting representations that possess robustness to such variations naturally.
3. Random Linear Fader (RLF): Introduces random temporal amplitude changes, resembling fade-in or fade-out effects, thus enabling the model to learn fading dynamics.

The authors incorporate a specially-designed encoder network combining statistics from various convolution layers' outputs to integrate local and global audio features effectively. The BYOL-A encoder architecturally merges local and global information by flattening and concatenating feature maps before final aggregation using temporal mean and max pooling.

Experimental Evaluations

The robustness of BYOL-A was assessed through a comprehensive benchmarking approach across multiple tasks, including sound event recognition (ESC-50, UrbanSound8K), non-semantic speech (VoxCeleb1, CREMA-D), and musical tasks (GTZAN, Surge synthesizer). The performance in these tasks established that representations generated by BYOL-A are generalizable, as the method exceeded or matched state-of-the-art techniques in multiple evaluations, demonstrating versatility in accommodating both generic and task-specific audio requirements.

Ablation Studies and Insights

In order to dissect the contributions of various components, detailed ablation studies were undertaken:

• The encoder architecture, which efficiently combines multi-layer features, emerged as the most substantial contributor to performance enhancements.
• The BYOL framework, while not heavily reliant on direct comparison like contrastive methods, still fundamentally aids the training process through a joint and constant updating of encoder targets.
• Data augmentation strategies like Mixup, RRC, and RLF were critical in training robust representations, though their utility varied across different experimental settings.

Theoretical and Practical Implications

Theoretically, the work enhances understanding of self-supervised strategies tailored for audio representation learning, extending the BYOL method beyond its image-processing origins. The practical implications stretch to creating efficient and versatile machine learning models that significantly reduce the need for task-specific pre-training, thus potentially lowering the computational cost and time associated with preparing models for diverse audio applications.

Future Directions

Building on the positive results, the research lays groundwork for future studies that might explore expanding the BYOL framework with more sophisticated or domain-specific augmentation techniques, potentially using deeper architectures like Transformers for audio processing. Alongside open-sourcing their code, the authors encourage exploratory and applied research, urging for continued advancement in the general-purpose audio representation domain.