Contrastive Learning of General-Purpose Audio Representations (2010.10915v1)

Abstract: We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio. Our approach is based on contrastive learning: it learns a representation which assigns high similarity to audio segments extracted from the same recording while assigning lower similarity to segments from different recordings. We build on top of recent advances in contrastive learning for computer vision and reinforcement learning to design a lightweight, easy-to-implement self-supervised model of audio. We pre-train embeddings on the large-scale Audioset database and transfer these representations to 9 diverse classification tasks, including speech, music, animal sounds, and acoustic scenes. We show that despite its simplicity, our method significantly outperforms previous self-supervised systems. We furthermore conduct ablation studies to identify key design choices and release a library to pre-train and fine-tune COLA models.

• The paper presents a novel self-supervised framework (COLA) that uses contrastive learning to generate robust audio embeddings.
• It demonstrates that simple segmentation and bilinear similarity enhance efficiency and achieve near-supervised performance across nine tasks.
• Experimental results show that COLA outperforms established benchmarks in speech, music, and environmental sound recognition when fine-tuned.

Contrastive Learning of General-Purpose Audio Representations

The paper "Contrastive Learning of General-Purpose Audio Representations" presents an advanced method known as COLA (COntrastive Learning for Audio), which contributes to the field of audio representation learning by utilizing a self-supervised pre-training approach. The authors propose a framework that leverages contrastive learning techniques akin to those used in computer vision and reinforcement learning, specifically tailored to extract meaningful features from audio data.

Methodology

COLA employs a contrastive learning paradigm that focuses on assigning high similarity scores to audio segments originating from the same recording, while ensuring lower similarity scores for segments from separate recordings. This task is facilitated through a lightweight, self-supervised model that can be easily implemented. Pre-training is conducted using the expansive Audioset database, with the resulting representations being tested across nine distinct classification tasks. These tasks include areas such as speech recognition, music categorization, discerning animal sounds, and identifying acoustic scenes.

COLA differs from traditional approaches such as generative encoder-decoder models by eliminating the need for input reconstruction, resulting in computational efficiency. The authors adopt simple methods of segmentation to generate positive (from the same audio clip) and negative pairs (from different clips), bypassing the necessity for sample augmentation or complex negative sample selection typically seen in triplet-based approaches.

Experimental Results

The experimentation highlights the robustness of COLA's representations across a broad range of tasks. Results show that, with simple linear classifiers built on frozen COLA embeddings, the method achieves test accuracies within close proximity to those from fully supervised convolutional models, even outperforming these supervised models when fine-tuned. For example, across several speech and non-speech datasets, pre-trained COLA embeddings outperform established self-supervised benchmarks like TRILL and triplet-loss based approaches substantially.

Furthermore, ablation studies confirm that different computational choices, such as similarity measures and batch sizes, influence outcomes. Bilinear similarity was found to consistently outperform cosine similarity, contributing to the effectiveness of representation learning in this framework.

Implications and Future Research

The implications of this research extend to both theoretical and practical domains in the field of machine learning. By standardizing a simple yet effective methodology for self-supervised audio representation learning, COLA paves the way for improving various audio-centric AI applications such as automatic speech recognition, environmental sound classification, and music recommendation systems.

This work also opens avenues for future research in exploring advanced contrastive techniques and optimizing scalable, general-purpose audio models. Investigating the integration of such frameworks within real-time applications, or adapting COLA for other domains intersecting with audio analysis, presents further potential for development.

In conclusion, COLA introduces a significant contribution to the field of audio machine learning via its innovative application of contrastive learning. With its demonstrated performance and generalizability across diverse audio tasks, it establishes a solid baseline and encourages the pursuit of enhanced audio analysis systems.