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Found 12 papers, 3 papers with code
AudioLM: a Language Modeling Approach to Audio Generation
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.
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.
SEANet: A Multi-modal Speech Enhancement Network
We explore the possibility of leveraging accelerometer data to perform speech enhancement in very noisy conditions.
Learning to Denoise Historical Music
We propose an audio-to-audio neural network model that learns to denoise old music recordings.
Training Keyword Spotters with Limited and Synthesized Speech Data
With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords.
Ranked #10 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V2 12 metric)
SPICE: Self-supervised Pitch Estimation
We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation.
Learning audio representations via phase prediction
We learn audio representations by solving a novel self-supervised learning task, which consists of predicting the phase of the short-time Fourier transform from its magnitude.
From Here to There: Video Inbetweening Using Direct 3D Convolutions
We first obtain a latent video representation using a stochastic fusion mechanism that learns how to incorporate information from the start and end frames.
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.
An Empirical Study of Generative Models with Encoders
Generative adversarial networks (GANs) are capable of producing high quality image samples.
Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition
Low power digital signal processors (DSPs) typically have a very limited amount of memory in which to cache data.
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.
