Masaru Sugano

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Audio information classification becomes a very important task for such purposes as automatic keyword spotting and other content-based audio-visual query system. In this paper, we describe a fast and accurate audio data classification method on MPEG coded data domain. Firstly silent segments are detected using a robust approach for different recording(More)
1. Briefly, what approach or combination of approaches did you test in each of your submitted runs? 1_kddi_ss_base1_5: “Baseline” method based on SVM, which discriminates shots that contain story boundaries. 1_kddi_ss_c+k1_4: Baseline + section-specialized segmentation (SS-S). 1_kddi_ss_all1_3: Baseline + SS-S + anchor shot segmentation (ASS) based on audio(More)
In this paper, we present a pioneering study on genre classification for home video. Analyzing home video is a challenging problem because it is generally unstructured and of low production quality in audio and video. Our approach is to define a set of genres referring to those in the actual video sharing site, and to extract salient low level features from(More)
Formerly, once the audio data is compressed, transcoding is used to scale the bit rate, where decoding and re-encoding are taken place. Therefore, data manipulation of coded data has been very complex and time consuming work. In this paper, we describe three algorithms for bit rate scaling on coded MPEG data domain. One is bandwidth limitation method(More)
This paper proposes shot genre classification from MPEG compressed movies, as one of the high-level indexing methods for audio-visual contents. Through statistical analysis of low-level and mid-level audio-visual features on compressed domain, the proposed method can achieve subjectively accurate shot classification within the movies into predefined genre(More)