A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive feature detection using support vector machines
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Calculation of a composite DET curve
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Prominence Detection Using Auditory Attention Cues and Task-Dependent High Level Information
IEEE Transactions on Audio, Speech, and Language Processing
Advances in Missing Feature Techniques for Robust Large-Vocabulary Continuous Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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Auditory salience describes how much a particular auditory event attracts human attention. Previous attempts at automatic detection of salient audio events have been hampered by the challenge of defining ground truth. In this paper ground truth for auditory salience is built up from annotations by human subjects of a large corpus of meeting room recordings. Following statistical purification of the data, an optimal auditory salience filter with linear discrimination is derived from the purified data. An automatic auditory salience detector based on optimal filtering of the Bark-frequency loudness performs with 32% equal error rate. Expanding the feature vector to include other common feature sets does not improve performance. Consistent with intuition, the optimal filter looks like an onset detector in the time domain.