CellNet applies regression-based deep learning to count cells from sparse point annotations in microscopy images and claims better performance than zero-shot methods in low-data regimes.
Feature Learning for Chord Recognition: The Deep Chroma Extractor
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough information to model harmonic content of audio for chord recognition, but that standard chroma extractors compute too noisy features. This leads us to propose a learned chroma feature extractor based on artificial neural networks. It is trained to compute chroma features that encode harmonic information important for chord recognition, while being robust to irrelevant interferences. We achieve this by feeding the network an audio spectrum with context instead of a single frame as input. This way, the network can learn to selectively compensate noise and resolve harmonic ambiguities. We compare the resulting features to hand-crafted ones by using a simple linear frame-wise classifier for chord recognition on various data sets. The results show that the learned feature extractor produces superior chroma vectors for chord recognition.
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cs.CV 1years
2026 1verdicts
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CellNet -- Localizing Cells using Sparse and Noisy Point Annotations
CellNet applies regression-based deep learning to count cells from sparse point annotations in microscopy images and claims better performance than zero-shot methods in low-data regimes.