Matching Text and Audio Embeddings: Exploring Transfer-learning Strategies for Language-based Audio Retrieval
This work addresses language-based audio retrieval for applications like multimedia search, but it is incremental as it builds on existing models and methods.
The paper tackles cross-modal text-to-audio retrieval by analyzing large-scale pretrained models like RoBERTa and PANNs in a metric learning framework, achieving improved performance through pretraining with noisy text-audio pairs from Freesound and optimizing loss functions and fine-tuning strategies.
We present an analysis of large-scale pretrained deep learning models used for cross-modal (text-to-audio) retrieval. We use embeddings extracted by these models in a metric learning framework to connect matching pairs of audio and text. Shallow neural networks map the embeddings to a common dimensionality. Our system, which is an extension of our submission to the Language-based Audio Retrieval Task of the DCASE Challenge 2022, employs the RoBERTa foundation model as the text embedding extractor. A pretrained PANNs model extracts the audio embeddings. To improve the generalisation of our model, we investigate how pretraining with audio and associated noisy text collected from the online platform Freesound improves the performance of our method. Furthermore, our ablation study reveals that the proper choice of the loss function and fine-tuning the pretrained models are essential in training a competitive retrieval system.