2015 |
A Zlatintsi, E.Iosif, P Maragos, A Potamianos Audio Salient Event Detection and Summarization using Audio and Text Modalities Conference Nice, France, 2015. Abstract | BibTeX | Links: [PDF] @conference{ZIM+15, title = {Audio Salient Event Detection and Summarization using Audio and Text Modalities}, author = {A Zlatintsi and E.Iosif and P Maragos and A Potamianos}, url = {http://robotics.ntua.gr/wp-content/publications/ZlatintsiEtAl_AudioTextSum-EUSIPCO-2015.pdf}, year = {2015}, date = {2015-09-01}, address = {Nice, France}, abstract = {This paper investigates the problem of audio event detection and summarization, building on previous work [1, 2] on the detection of perceptually important audio events based on saliency models. We take a synergistic approach to audio summarization where saliency computation of audio streams is assisted by using the text modality as well. Auditory saliency is assessed by auditory and perceptual cues such as Teager energy, loudness and roughness; all known to correlate with attention and human hearing. Text analysis incorporates part-of-speech tagging and affective modeling. A computational method for the automatic correction of the boundaries of the selected audio events is applied creating summaries that consist not only of salient but also meaningful and semantically coherent events. A non-parametric classification technique is employed and results are reported on the MovSum movie database using objective evaluations against ground-truth designating the auditory and semantically salient events.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } This paper investigates the problem of audio event detection and summarization, building on previous work [1, 2] on the detection of perceptually important audio events based on saliency models. We take a synergistic approach to audio summarization where saliency computation of audio streams is assisted by using the text modality as well. Auditory saliency is assessed by auditory and perceptual cues such as Teager energy, loudness and roughness; all known to correlate with attention and human hearing. Text analysis incorporates part-of-speech tagging and affective modeling. A computational method for the automatic correction of the boundaries of the selected audio events is applied creating summaries that consist not only of salient but also meaningful and semantically coherent events. A non-parametric classification technique is employed and results are reported on the MovSum movie database using objective evaluations against ground-truth designating the auditory and semantically salient events. |
2012 |
A Zlatintsi, P Maragos, A Potamianos, G Evangelopoulos A Saliency-Based Approach to Audio Event Detection and Summarization Conference Proc. European Signal Processing Conference, Bucharest, Romania, 2012. Abstract | BibTeX | Links: [PDF] @conference{ZMP+12, title = {A Saliency-Based Approach to Audio Event Detection and Summarization}, author = {A Zlatintsi and P Maragos and A Potamianos and G Evangelopoulos}, url = {http://robotics.ntua.gr/wp-content/publications/ZlatintsiMaragos+_SaliencyBasedAudioSummarization_EUSIPCO2012.pdf}, year = {2012}, date = {2012-08-01}, booktitle = {Proc. European Signal Processing Conference}, address = {Bucharest, Romania}, abstract = {In this paper, we approach the problem of audio summarization by saliency computation of audio streams, exploring the potential of a modulation model for the detection of perceptually important audio events based on saliency models, along with various fusion schemes for their combination. The fusion schemes include linear, adaptive and nonlinear methods. A machine learning approach, where training of the features is performed, was also applied for the purpose of comparison with the proposed technique. For the evaluation of the algorithm we use audio data taken from movies and we show that nonlinear fusion schemes perform best. The results are reported on the MovSum database, using objective evaluations (against ground-truth denoting the perceptually important audio events). Analysis of the selected audio segments is also performed against a labeled database in respect to audio categories, while a method for fine-tuning of the selected audio events is proposed.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In this paper, we approach the problem of audio summarization by saliency computation of audio streams, exploring the potential of a modulation model for the detection of perceptually important audio events based on saliency models, along with various fusion schemes for their combination. The fusion schemes include linear, adaptive and nonlinear methods. A machine learning approach, where training of the features is performed, was also applied for the purpose of comparison with the proposed technique. For the evaluation of the algorithm we use audio data taken from movies and we show that nonlinear fusion schemes perform best. The results are reported on the MovSum database, using objective evaluations (against ground-truth denoting the perceptually important audio events). Analysis of the selected audio segments is also performed against a labeled database in respect to audio categories, while a method for fine-tuning of the selected audio events is proposed. |
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