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Found 10 papers, 5 papers with code
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments.
Simultaneous linear connectivity of neural networks modulo permutation
In contrast, the claim "strong linear connectivity"-that for each network, there exists one permutation that simultaneously connects it with the other networks-is both intuitively and practically more desirable.
All Thresholds Barred: Direct Estimation of Call Density in Bioacoustic Data
We use these distributions to predict site-level densities, which may be subject to distribution shifts.
BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
The ability for a machine learning model to cope with differences in training and deployment conditions--e. g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases.
Global birdsong embeddings enable superior transfer learning for bioacoustic classification
With the advent of deep learning models, classification of important signals from these datasets has markedly improved.
In Search for a Generalizable Method for Source Free Domain Adaptation
Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data.
Ultra-Low-Bitrate Speech Coding with Pretrained Transformers
Our numerical experiments show that supplementing the convolutional encoder of a neural speech codec with Transformer speech embeddings yields a speech codec with a bitrate of $600\,\mathrm{bps}$ that outperforms the original neural speech codec in synthesized speech quality when trained at the same bitrate.
Improving Bird Classification with Unsupervised Sound Separation
This paper addresses the problem of species classification in bird song recordings.
Handling Background Noise in Neural Speech Generation
Recent advances in neural-network based generative modeling of speech has shown great potential for speech coding.
Generative Speech Coding with Predictive Variance Regularization
We introduce predictive-variance regularization to reduce the sensitivity to outliers, resulting in a significant increase in performance.
