A report on sound event detection with different binaural features
arXiv:1710.02997
Abstract
In this paper, we compare the performance of using binaural audio features in place of single-channel features for sound event detection. Three different binaural features are studied and evaluated on the publicly available TUT Sound Events 2017 dataset of length 70 minutes. Sound event detection is performed separately with single-channel and binaural features using stacked convolutional and recurrent neural network and the evaluation is reported using standard metrics of error rate and F-score. The studied binaural features are seen to consistently perform equal to or better than the single-channel features with respect to error rate metric.
arXiv commentTechnical report for the top performing method in Task 3: Real life sound event detection challenge, at Detection and classification of acoustic scene and events (DCASE) 2017
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- Sound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering
- End-to-End Neural Speaker Diarization with Permutation-Free Objectives
- On the performance of residual block design alternatives in convolutional neural networks for end-to-end audio classification
- Sound Event Detection Utilizing Graph Laplacian Regularization with Event Co-occurrence
- Adaptive Multi-scale Detection of Acoustic Events