active learning for sound event classification by

ACTIVE LEARNING FOR SOUND EVENT CLASSIFICATION BY CLUSTERING UNLABELED DATA Zhao Shuyang, Toni Heittola, Tuomas Virtanen Department of Signal Processing, Tampere University of Technology, Finland Background

Evaluation Dataset: UrbanSound 8K

Training sound event classifier requires annotated recordings: I I

Audio data is easy to collect. Annotation is time-consuming. Idea: Utilizing abundant unlabeled data to optimize the effectiveness of the annotation effort.

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8732 labeled sound segments

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10 sound event classes in open urban space.

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Cross-validation: 10-fold. Setup

Proposed method Medoid-based active learning (MAL): parition the data and annotate only the medoid segments, centroids of clusters. I I

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Labels are produced by simulating limited number of labeling responses (labeling budget), according to the ground truth.

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Number of clusters is set to 1/4 of the number of unlabeled data points.

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Supervised learning setup (feature and model) follows UrbanSound SVM baseline. Features are various MFCCs statistics within segments: mean, median, variance, minimum, maximum, skewness, etc.

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Compared with reference methods, including random sample (baseline), certainty-based active learning(CRTAL) and semi-supervised learning (SSL).

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Experiments are repeated five times and the average performance is reported.

Medoids are assured to span different local distributions. A labeled medoid can be used to derive predicted labels for other cluster members.

Result

Figure 1: Overview of the proposed method. Medoid segments are marked with red border. Annotated labels are filled with black and predicted labels are filled with grey.

Noticeable technical details I

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In the clustering stage, sound segments are respresented with a single Gaussian, based on the MFCCs statistics. Segment-segment dissimilarity is measured by the symmetric KL divergence.

Figure 2: Classification accuracy as a function of labeling budget, simulated using an oracle annotator. I

The classification accuracy is improved by 8%, when labeling budget is lower than 10% of unlabeled data.

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The proposed method saves 50% to 60% budget to achieve the same accuracy, with respect to the best reference method.

Conclusions I

The proposed method effectively saves labeling budget for sound event classification.

Initialization of mediods is based on farthest-first traversal, starting from a random point.

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Future: Study with different datasets, especially larger scale datasets.

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An annotated label overrules predicted labels on the same segments.

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Future: Study weak annotator case, simulated using real human labeling responses.

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Produced labels are used to construct training examples for supervised learning.

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K-medoids clustering is based on the dissimilarity matrix.

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