Leveraging Bi-modal Semantic Similarity for Weakly-supervised Audio Source Separation

Основные понятия

The proposed framework leverages bi-modal semantic similarity between audio and language modalities to generate weak supervision signals for single-source audio extraction, without requiring access to single-source audio samples during training.

Аннотация

The paper presents a weakly-supervised audio separation framework that can extract single-source audio signals from a mixture, using corresponding language descriptions as conditioning signals. The key idea is to leverage the fact that single-source language entities can be easily extracted from the text descriptions, and use a pretrained joint audio-language embedding model (CLAP) to generate weak supervision signals for the audio separation model.

The framework consists of three main components:

1. Unsupervised Mix-and-Separate Training: The audio separation model is trained on synthetic mixtures of audio samples, using an unsupervised reconstruction loss.

2. Weakly Supervised Audio-Language Training: The model is further trained using a bi-modal contrastive loss that aligns the predicted single-source audio with the corresponding language entities. This provides weak supervision for single-source extraction without requiring access to single-source audio samples.

3. Consistency Reconstruction Loss: An additional consistency loss is introduced to ensure the predicted single-source components sum up to the original mixture.

The authors show that this weakly-supervised framework can significantly boost the performance of unsupervised audio separation baselines, achieving up to 71% improvement in Signal-to-Distortion Ratio (SDR) over the baseline. Furthermore, the framework can also be used to augment supervised audio separation models, leading to a powerful semi-supervised learning approach that outperforms the supervised baseline by up to 17% in SDR.

Extensive experiments are conducted on synthetic mixtures from the MUSIC, VGGSound, and AudioCaps datasets, demonstrating the effectiveness of the proposed approach across different scenarios.

Статистика

The paper reports the following key metrics:

On the MUSIC dataset, the proposed weakly-supervised framework achieves 97.5% of the supervised method's performance on 2-source mixtures.
Compared to the unsupervised mix-and-separate baseline, the proposed method achieves 71%, 102%, and 129% SDR boost on 2-source, 3-source, and 4-source separation test sets, respectively.
In the semi-supervised setting, the proposed method outperforms the supervised baseline by 6.2 SDR on 2-source mixtures, when using only 5% of the supervised data.

Цитаты

"Our framework achieves this by making large corpora of unsupervised data available to the supervised learning model as well as utilizing a natural, robust regularization mechanism through weak supervision from the language modality, and hence enabling a powerful semi-supervised framework for audio separation."
"Notably, we achieve 97.5% of the supervised method's performance trained on 2-source mixtures."

Ключевые выводы из

Weakly-supervised Audio Separation via Bi-modal Semantic Similarity

by Tanvir Mahmu... в arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01740.pdf

Дополнительные вопросы

How can the proposed weakly-supervised framework be extended to other modalities beyond audio-language, such as image-text or video-text

The proposed weakly-supervised framework can be extended to other modalities beyond audio-language, such as image-text or video-text, by adapting the core concept of leveraging pretrained joint embedding models between the modalities. For image-text scenarios, models like CLIP (Contrastive Language-Image Pretraining) can be utilized to map images and text into a shared semantic space. Similarly, for video-text applications, models that can encode both video and text, such as CLIP, can be employed. The process would involve generating weak supervision signals by calculating similarity scores between the model's predictions in one modality and the conditioning prompts in the other modality. This approach enables the model to learn to extract single-source signals from mixtures in the target modality using easily separable corresponding signals in the conditioning modality.

What are the potential limitations of the CLAP-based weak supervision, and how could it be further improved or complemented by other techniques

One potential limitation of the CLAP-based weak supervision is the reliance on the quality and coverage of the pretrained joint embedding model. If the CLAP model does not adequately capture the semantic relationships between audio and language modalities, it may lead to suboptimal weak supervision signals. To address this limitation, the CLAP model could be further fine-tuned on specific tasks related to audio-language interactions to enhance its performance in generating meaningful weak supervision signals. Additionally, incorporating domain-specific knowledge or additional data augmentation techniques could help improve the robustness and effectiveness of the weak supervision approach. Complementing the CLAP-based weak supervision with other techniques, such as self-supervised learning or data augmentation strategies, could further enhance the model's performance and generalization capabilities.

Given the strong performance of the semi-supervised approach, how could the proposed framework be leveraged to enable efficient and scalable audio separation models in real-world applications

The strong performance of the semi-supervised approach opens up possibilities for efficient and scalable audio separation models in real-world applications. To leverage the proposed framework for real-world applications, several strategies can be implemented:

Large-Scale Data Utilization: Expanding the training dataset to include a diverse range of audio mixtures and corresponding text descriptions can improve the model's ability to generalize to various scenarios.
Online Learning: Implementing an online learning approach where the model continuously learns from new data streams can ensure the model stays updated and adapts to changing audio separation requirements.
Deployment in Production Systems: Integrating the trained model into production systems for real-time audio separation tasks can enhance the efficiency and effectiveness of audio processing workflows.
Model Optimization: Continuously optimizing the model architecture and training process based on feedback from real-world usage can further enhance the model's performance and scalability.
By incorporating these strategies, the proposed framework can be effectively utilized to develop robust and scalable audio separation models for a wide range of real-world applications.