Abstract: A method of machine learning network includes receiving one or more sound segments and one or more associated text labels indicating captions associated with the sound segments, generating, utilizing a large language model of the machine learning network, one or more counterfactual captions associated with the one or more sound segments, wherein the one or more counterfactual captions are adversarial captions, determining a loss associated with the one or more sound segments, one or more associated text labels, and one or more counterfactual captions, updating parameters associated with an audio encoder or text encoder of the machine learning network, in response to falling below a threshold, repeating steps list above, and in response to meeting the threshold and utilizing a ranking, updating final parameters associated with the machine learning network.

Abstract: Methods and systems for training an audio-based machine learning model to predict a health condition based on biological sounds emitted by a person. Audio data corresponding to biological sounds produced by the person is generated from a microphone. The audio data is segmented into a plurality of segments, each segment associated with a respective sound event. An audio-based machine learning model is executed on the plurality of segments. The audio-based machine learning model is configured to output, for each segment, a label of a medical condition and an associated a confidence score. The model is trained via active learning, in which a subset of the plurality of segments are selected based on their confidence score being below a threshold, and provided to a human for annotation.

Abstract: Systems and methods for cross-modal retrieval are provided. According to one aspect, a method for cross-modal retrieval includes obtaining a query describing a sound using a query modality other than a sound modality; encoding the query to obtain a query embedding using a query encoder network for the query modality and a query projection network, wherein the query projection network includes a self-attention layer, and wherein the query embedding is in a joint embedding space for the query modality and the sound modality; and providing a response including an audio sample based on the query embedding, wherein the audio sample includes the sound.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.