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Found 8 papers, 6 papers with code
Estimating Visual Information From Audio Through Manifold Learning
Therefore, audio-based methods can be useful even for applications in which only visual information is of interest Our framework is based on Manifold Learning and consists of two steps.
HEAR: Holistic Evaluation of Audio Representations
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
One Billion Audio Sounds from GPU-enabled Modular Synthesis
We release synth1B1, a multi-modal audio corpus consisting of 1 billion 4-second synthesized sounds, paired with the synthesis parameters used to generate them.
Deep Autotuner: a Pitch Correcting Network for Singing Performances
We train our neural network model using a dataset of 4, 702 amateur karaoke performances selected for good intonation.
Deep Autotuner: A Data-Driven Approach to Natural-Sounding Pitch Correction for Singing Voice in Karaoke Performances
This approach differs from commercially used automatic pitch correction systems, where notes in the vocal tracks are shifted to be centered around notes in a user-defined score or mapped to the closest pitch among the twelve equal-tempered scale degrees.
Espresso: Efficient Forward Propagation for Binary Deep Neural Networks
Binary Deep Neural Networks (BDNNs) have been shown to be an effective way of achieving this objective.
Espresso: Efficient Forward Propagation for BCNNs
In this paper, we show how Convolutional Neural Networks (CNNs) can be implemented using binary representations.
The Orchive : Data mining a massive bioacoustic archive
The Orchive is a large collection of over 20, 000 hours of audio recordings from the OrcaLab research facility located off the northern tip of Vancouver Island.
