scholarly journals Underground pipeline surveillance with an algorithm based on statistical time-frequency acoustic features

2022 ◽  
Vol 27 (2) ◽  
pp. 358-371
Author(s):  
Tianlei Wang ◽  
Jiuwen Cao ◽  
Ru Xu ◽  
Jianzhong Wang
Keyword(s):  
Underground Pipeline ◽  
Acoustic Features ◽  
Time Frequency ◽  
Statistical Time

A Novel Underground Pipeline Surveillance System Based on Hybrid Acoustic Features

2021 ◽  
Vol 21 (2) ◽  
pp. 1040-1050
Author(s):  
Tianlei Wang ◽  
Jiuwen Cao ◽  
Leiyu Pei
Keyword(s):  
Surveillance System ◽  
Underground Pipeline ◽  
Acoustic Features

scholarly journals Statistical Time-Frequency Multiplexing of HD Video Traffic in DVB-T2

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Mehdi Rezaei ◽  
Imed Bouazizi ◽  
Moncef Gabbouj
Keyword(s):  
High Performance ◽  
Transmission Scheme ◽  
High Definition ◽  
Time Frequency ◽  
Proposed Model ◽  
Frequency Domains ◽  
Video Broadcast ◽  
Statistical Time ◽  
Digital Video Broadcast

Digital video broadcast-terrestrial 2 (DVB-T2) is the successor of DVB-T standard that allows a two-dimensional multiplexing of broadcast services in time and frequency domains. It introduces an optional time-frequency slicing (TFS) transmission scheme to increase the flexibility of service multiplexing. Utilizing statistical multiplexing (StatMux) in conjunction with TFS is expected to provide a high performance for the broadcast system in terms of resource utilization and quality of service. In this paper, a model for high-definition video (HDV) traffic is proposed. Then, utilizing the proposed model, the performance of StatMux of HDV broadcast services over DVB-T2 is evaluated. Results of the study show that implementation of StatMux in conjunction with the newly available features in DVB-T2 provides a high performance for the broadcast system.

scholarly journals Encouraging Attention and Exploration in a Hybrid Recommender System for Libraries of Unfamiliar Music

2019 ◽  
Vol 2 ◽  
pp. 205920431989317
Author(s):  
John R. Taylor ◽  
Roger T. Dean
Keyword(s):  
User Interaction ◽  
Acoustic Features ◽  
Music Preferences ◽  
Continuous Response ◽  
Starting Conditions ◽  
Perceived Familiarity ◽  
Music Centre ◽  
Statistical Time ◽  
Music Information ◽  
Hybrid Recommender

There are few studies of user interaction with music libraries comprising solely of unfamiliar music, despite such music being represented in national music information centre collections. We aim to develop a system that encourages exploration of such a library. This study investigates the influence of 69 users’ pre-existing musical genre and feature preferences on their ongoing continuous real-time psychological affect responses during listening and the acoustic features of the music on their liking and familiarity ratings for unfamiliar art music (the collection of the Australian Music Centre) during a sequential hybrid recommender-guided interaction. We successfully mitigated the unfavorable starting conditions (no prior item ratings or participants’ item choices) by using each participant’s pre-listening music preferences, translated into acoustic features and linked to item view count from the Australian Music Centre database, to choose their seed item. We found that first item liking/familiarity ratings were on average higher than the subsequent 15 items and comparable with the maximal values at the end of listeners’ sequential responses, showing acoustic features to be useful predictors of responses. We required users to give a continuous response indication of their perception of the affect expressed as they listened to 30-second excerpts of music, with our system successfully providing either a “similar” or “dissimilar” next item, according to—and confirming—the utility of the items’ acoustic features, but chosen from the affective responses of the preceding item. We also developed predictive statistical time series analysis models of liking and familiarity, using music preferences and preceding ratings. Our analyses suggest our users were at the starting low end of the commonly observed inverted-U relationship between exposure and both liking and perceived familiarity, which were closely related. Overall, our hybrid recommender worked well under extreme conditions, with 53 unique items from 100 chosen as “seed” items, suggesting future enhancement of our approach can productively encourage exploration of libraries of unfamiliar music.

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