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Found 4 papers, 1 papers with code
Training sound event detection with soft labels from crowdsourced annotations
In this paper, we study the use of soft labels to train a system for sound event detection (SED).
Low-complexity acoustic scene classification in DCASE 2022 Challenge
The provided baseline system is a convolutional neural network which employs post-training quantization of parameters, resulting in 46. 5 K parameters, and 29. 23 million multiply-and-accumulate operations (MMACs).
Crowdsourcing strong labels for sound event detection
Strong labels are a necessity for evaluation of sound event detection methods, but often scarcely available due to the high resources required by the annotation task.
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems
The most used techniques among the submissions were residual networks and weight quantization, with the top systems reaching over 70% accuracy, and log loss under 0. 8.
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