Well being acoustics, encompassing appears like coughs and respiration, maintain priceless well being data however should be utilized extra in medical machine studying. Present deep studying fashions for these acoustics are sometimes task-specific, limiting their generalizability. Non-semantic speech attributes can assist in emotion recognition and detecting ailments like Parkinson’s and Alzheimer’s. Current developments in SSL promise to allow fashions to be taught sturdy, normal representations from giant, unlabeled information. Whereas SSL has progressed in fields like imaginative and prescient and language, its software to well being acoustics stays largely unexplored.
Researchers from Google Analysis and the Heart of Infectious Illness Analysis in Zambia developed HeAR, a scalable deep-learning system primarily based on SSL. HeAR makes use of masked autoencoders educated on an enormous dataset of 313 million two-second audio clips. The mannequin establishes itself as state-of-the-art for well being audio embeddings, excelling throughout 33 well being acoustic duties from 6 datasets. HeAR’s low-dimensional representations, derived from SSL, present sturdy transferability and generalization to out-of-distribution information, outperforming current fashions on capabilities comparable to well being occasion detection, cough inference, and spirometry throughout varied datasets.
SSL has grow to be a key method for creating normal representations from giant, unannotated datasets. Varied SSL strategies, comparable to contrastive (SimCLR, BYOL) and generative (MAE), have superior, particularly in audio processing. Current progress in SSL-based audio encoders, like Wav2vec 2.0 and AudioMAE, has considerably improved speech illustration studying. Whereas non-semantic speech SSL, comparable to TRILL and FRILL, has seen some improvement, non-semantic well being acoustics nonetheless should be explored. This examine introduces a generative SSL framework (MAE) targeted on non-semantic well being acoustics, aiming to enhance generalization in well being monitoring and illness detection duties.
HeAR consists of three major elements: information curation (together with a well being acoustic occasion detector), general-purpose coaching for creating an audio encoder, and task-specific analysis utilizing the educated embeddings. The system encodes two-second audio clips to generate embeddings for downstream duties. The well being acoustic occasion detector, a CNN, identifies six non-speech well being occasions like coughing and respiration. HeAR is educated on a big dataset (YT-NS) of 313.3 million audio clips utilizing masked autoencoders. It’s benchmarked throughout varied well being acoustic duties, demonstrating superior efficiency in comparison with state-of-the-art audio encoders like TRILL, FRILL, and CLAP.
HeAR outperformed different fashions throughout 33 duties on six datasets, attaining the best imply reciprocal rank (0.708) and rating first in 17 duties. Whereas CLAP excelled in well being acoustic detection (MRR=0.846), HeAR ranked second (MRR=0.538) regardless of not utilizing FSD50K for coaching. HeAR’s efficiency dropped with longer sequences, seemingly attributable to its fastened sinusoidal positional encodings. HeAR persistently outperformed baselines in a number of classes for cough inference and spirometry duties, demonstrating robustness and minimal efficiency variation throughout totally different recording gadgets, particularly in difficult datasets like CIDRZ and SpiroSmart.
The examine launched and assessed the HeAR system, which mixes a well being acoustic occasion detector with a generative learning-based audio encoder educated on YT-NS with out professional information curation. The system demonstrated sturdy efficiency throughout well being acoustic duties, comparable to tuberculosis classification from cough sounds and lung perform monitoring by way of smartphone audio. HeAR’s self-supervised studying mannequin proved efficient regardless of restricted information, exhibiting robustness throughout recording gadgets. Nevertheless, additional validation is required, particularly contemplating dataset biases and generalization limits. Future analysis ought to discover mannequin fine-tuning, on-device processing, and bias mitigation.
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