Relationship between classifier’s classification UARs and confidence scores for 500 and 5,000 initial training instances.

<p>Relationship between classifier’s classification UARs and confidence scores for 500 and 5,000 initial training instances.</p

Certainty-based Active Learning algorithm in a stream-based scenario.

<p>Certainty-based Active Learning algorithm in a stream-based scenario.</p

Learning curves for using active and passive learning method in stream-based scenario.

<p>Learning curves for using active and passive learning method in stream-based scenario.</p

Best performances up to statistic significance achieved using semi-supervised active learning (SSAL), active learning (AL), and passive learning (PL) in pool-based and stream-based scenarios, as well as the number of human-labeled instances (#HLI) needed to achieve that performance.

<p>Best performances up to statistic significance achieved using semi-supervised active learning (SSAL), active learning (AL), and passive learning (PL) in pool-based and stream-based scenarios, as well as the number of human-labeled instances (#HLI) needed to achieve that performance.</p

Learning curves for semi-supervised active learning (in each round 500 instances with lowest confidence scores are selected for human annotation and a variable number of instances with confidence scores above the threshold 0.95 are selected for machine annotation), active learning, and passive learning in the pool-based scenario.

<p>Learning curves for semi-supervised active learning (in each round 500 instances with lowest confidence scores are selected for human annotation and a variable number of instances with confidence scores above the threshold 0.95 are selected for machine annotation), active learning, and passive learning in the pool-based scenario.</p

Semi-supervised learning results for varying sizes of the initial training set (different number of human labeled instances) in combination with different confidence thresholds.

<p>Semi-supervised learning results for varying sizes of the initial training set (different number of human labeled instances) in combination with different confidence thresholds.</p

Semi-Supervised Active Learning in a stream-based scenario.

<p>Semi-Supervised Active Learning in a stream-based scenario.</p

Description of the subset of the FindSounds database used in this paper.

<p>Description of the subset of the FindSounds database used in this paper.</p

Overview of state-of-the-art research in sound classification.

<p>For features, BoAP: bag-of-audio-phrases descriptor, UFL: unsupervised feature learning, E: energy, SF: spectral features, ZCR: zero-crossing rate, TFB-ED: triangle filter bank and eigen-decomposition, MFCC: mel-frequency cepstral coefficients, STE: subband temporal envelopes, and for classifiers, SVM: support vector machines, RF: random forest, KFDA: kernel Fisher discriminant anlysis, HMM: hidden Markov models, for learning methods, FS: fully supervised learning.</p

Distribution percentage of classifier confidence scores for 500 (blue) and 5,000 (red) training instances.

<p>(There is no instance assigned with confidence score falling in the range of [0.0, 0.1].)</p