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We explore self-supervision as a way to learn general purpose audio representations. Specifically, we propose two self-supervised tasks: Audio2Vec, which aims at reconstructing a spectrogram slice from past and future slices and Temporal-Gap, which estimates the distance between two short audio segments extracted at random from the same audio clip. We evaluate how the representations learned via self-supervision transfer to different downstream tasks, either training a task-specific linear classifier on top of the pretrained embeddings, or fine-tuning a model end-to-end for each downstream task. Our results show that the representations learned with Audio2Vec transfer better than those learned by fully-supervised training on Audioset. In addition, by fine-tuning Audio2Vec representations it is possible to outperform fully-supervised models trained from scratch on each task, when limited data is available, thus improving label efficiency.

We explore self-supervised models that can be potentially deployed on mobile devices to learn general purpose audio representations. Specifically, we propose methods that exploit the temporal context in the spectrogram domain. One method estimates the temporal gap between two short audio segments extracted at random from the same audio clip. The other methods are inspired by Word2Vec, a popular technique used to learn word embeddings, and aim at reconstructing a temporal spectrogram slice from past and future slices or, alternatively, at reconstructing the context of surrounding slices from the current slice. We focus our evaluation on small encoder architectures, which can be potentially run on mobile devices during both inference (re-using a common learned representation across multiple downstream tasks) and training (capturing the true data distribution without compromising users' privacy when combined with federated learning). We evaluate the quality of the embeddings produced by the self-supervised learning models, and show that they can be re-used for a variety of downstream tasks, and for some tasks even approach the performance of fully supervised models of similar size.Preprint. Under review.

Mel-filterbanks are fixed, engineered audio features which emulate human perception and have been used through the history of audio understanding up to today. However, their undeniable qualities are counterbalanced by the fundamental limitations of handmade representations. In this work we show that we can train a single learnable frontend that outperforms mel-filterbanks on a wide range of audio signals, including speech, music, audio events and animal sounds, providing a general-purpose learned frontend for audio classification. To do so, we introduce a new principled, lightweight, fully learnable architecture that can be used as a drop-in replacement of mel-filterbanks. Our system learns all operations of audio features extraction, from filtering to pooling, compression and normalization, and can be integrated into any neural network at a negligible parameter cost. We perform multi-task training on eight diverse audio classification tasks, and show consistent improvements of our model over mel-filterbanks and previous learnable alternatives. Moreover, our system outperforms the current state-of-the-art learnable frontend on Audioset, with orders of magnitude fewer parameters.

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The deployment of deep networks on mobile devices requires to efficiently use the scarce computational resources, expressed as either available memory or computing cost. When addressing multiple tasks simultaneously, it is extremely important to share resources across tasks, especially when they all consume the same input data, e.g., audio samples captured by the on-board microphones. In this paper we propose a multi-task model architecture that consists of a shared encoder and multiple task-specific adapters. During training, we learn the model parameters as well as the allocation of the task-specific additional resources across both tasks and layers. A global tuning parameter can be used to obtain different multi-task network configurations finding the desired trade-off between cost and the level of accuracy across tasks. Our results show that this solution significantly outperforms a multi-head model baseline. Interestingly, we observe that the optimal resource allocation depends on both the task intrinsic characteristics as well as on the targeted cost measure (e.g., memory or computing cost).

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