Efficient Advertisement Discovery for Audio Podcast Content Using Candidate Segmentation

Efficient Advertisement Discovery for Audio Podcast Content Using Candidate Segmentation

Nowadays, audio podcasting has been widely used by many online sites such as newspapers, web portals, journals, and so forth, to deliver audio content to users through download or subscription. Within 1 to 30 minutes long of one podcast story, it is often that multiple audio advertisements (ads) are inserted into and repeated, with each of a length of 5 to 30 seconds, at different locations. Au...

Robust and efficient digital audio watermarking using audio content analysis

Audio-visual segmentation for content-based retrieval

This paper reports recent work at ORL on segmentation of digital audio/video recordings. Firstly, we describe an audio segmentation algorithm that partitions a soundtrack into manageably sized segments for speech recognition. Secondly, we present an algorithm for detecting camera shot-break locations in the video. The output of these two algorithms is combined to produce a semantically meaningf...

Content analysis for audio classification and segmentation

In this paper, we present our study of audio content analysis for classification and segmentation, in which an audio stream is segmented according to audio type or speaker identity. We propose a robust approach that is capable of classifying and segmenting an audio stream into speech, music, environment sound, and silence. Audio classification is processed in two steps, which makes it suitable ...

Audio content analysis for online audiovisual data segmentation and classification

While current approaches for audiovisual data segmentation and classification are mostly focused on visual cues, audio signals may actually play a more important role in content parsing for many applications. An approach to automatic segmentation and classification of audiovisual data based on audio content analysis is proposed. The audio signal from movies or TV programs is segmented and class...