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تسجيل مستخدم جديدPower Pooling Operators and Confidence Learning for Semi-Supervised Sound Event Detection
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In recent years, the involvement of synthetic strongly labeled data,weakly labeled data and unlabeled data has drawn much research attentionin semi-supervised sound event detection (SSED). Self-training models carry out predictions without strong annotations and then take predictions with high probabilities as pseudo-labels for retraining. Such models have shown its effectiveness in SSED. However, probabilities are poorly calibrated confidence estimates, and samples with low probabilities are ignored. Hence, we introduce a method of learning confidence deliberately and retaining all data distinctly by applying confidence as weights. Additionally, linear pooling has been considered as a state-of-the-art aggregation function for SSED with weak labeling. In this paper, we propose a power pooling function whose coefficient can be trained automatically to achieve nonlinearity. A confidencebased semi-supervised sound event detection (C-SSED) framework is designed to combine confidence and power pooling. The experimental results demonstrate that confidence is proportional to the accuracy of the predictions. The power pooling function outperforms linear pooling at both error rate and F1 results. In addition, the C-SSED framework achieves a relative error rate reduction of 34% in contrast to the baseline model.
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In this paper, we present a method called HODGEPODGEfootnotemark[1] for large-scale detection of sound events using weakly labeled, synthetic, and unlabeled data proposed in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019
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This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries). The target of the systems is to provide not only the event class but also the event t
ime boundaries given that multiple events can be present in an audio recording. Another challenge of the task is to explore the possibility to exploit a large amount of unbalanced and unlabeled training data together with a small weakly labeled training set to improve system performance. The data are Youtube video excerpts from domestic context which have many applications such as ambient assisted living. The domain was chosen due to the scientific challenges (wide variety of sounds, time-localized events.. .) and potential industrial applications .
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