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Found 9 papers, 1 papers with code
Text-Driven Separation of Arbitrary Sounds
The second model, SoundFilter, takes a mixed source audio clip as an input and separates it based on a conditioning vector from the shared text-audio representation defined by SoundWords, making the model agnostic to the conditioning modality.
One-shot conditional audio filtering of arbitrary sounds
When trained on Librispeech, our model achieves an SI-SDR improvement of 14. 0 dB when separating one voice from a mixture of two speakers.
MicAugment: One-shot Microphone Style Transfer
A crucial aspect for the successful deployment of audio-based models "in-the-wild" is the robustness to the transformations introduced by heterogeneous acquisition conditions.
Sense and Learn: Self-Supervision for Omnipresent Sensors
Likewise, the learned representations with self-supervision are found to be highly transferable between related datasets, even when few labeled instances are available from the target domains.
Learning to Denoise Historical Music
We propose an audio-to-audio neural network model that learns to denoise old music recordings.
SPICE: Self-supervised Pitch Estimation
We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation.
Self-supervised audio representation learning for mobile devices
We explore self-supervised models that can be potentially deployed on mobile devices to learn general purpose audio representations.
Now Playing: Continuous low-power music recognition
To reduce battery consumption, a small music detector runs continuously on the mobile device's DSP chip and wakes up the main application processor only when it is confident that music is present.
