In music production, descriptive terminology is used to define perceived sound transformations. By understanding the underlying statistical features associated with these descriptions, we can aid the retrieval of contextually relevant processing parameters using natural language, and create intelligent systems capable of assisting in audio engineering. In this study, we present an analysis of a dataset containing descriptive terms gathered using a series of processing modules, embedded within a Digital Audio Workstation. By applying hierarchical clustering to the audio feature space, we show that similarity in term representations exists within and between transformation classes. Furthermore, the organisation of terms in low-dimensional timbre space can be explained using perceptual concepts such as size and dissonance. We conclude by performing Latent Semantic Indexing to show that similar groupings exist based on term frequency.
Social media is extensively used nowadays and is gaining popularity among the users with the increasing growth in the network capacity, connectivity, and speed. Moreover, affordable prices of data plans, especially mobile data packages, have considerably increased the use of multimedia by different users. This includes terrorists who use social media platforms to promote their ideology and intimidate their adversaries. It is therefore very important to develop automated solutions to semantically analyse online contents to assist law enforcement agencies in the preventive policing of online activities. A major challenge for the social media forensic analysis is to preserve the privacy of citizens who use online social networking platforms. This paper presents results of European H2020 project RED-Alert that aims to enable secure and privacy preserving data processing; hence the malicious content and the corresponding personality can be ethically tracked. We have mined seven social media channels for content and providing support for ten languages for analysis. Our proposed solution is designed to ensure security and policing of online contents by detecting terrorist material. We have used social network analysis, speech recognition, face and object detection besides audio event detection to extract information from online sources that are fed in a complex event processor. We have discussed the challenges and prospects of this work especially the need of analysing online contents while respecting European and national data protection laws notably GDPR. Index Terms-Contents analysis, social network analysis, multimedia forensics, complex event processing, data protection.
Social media is extensively used nowadays and is gaining popularity among the users with the increasing growth in the network capacity, connectivity, and speed. Moreover, affordable prices of data plans, especially mobile data packages, have considerably increased the use of multimedia by different users. This includes terrorists who use social media platforms to promote their ideology and intimidate their adversaries. It is therefore very important to develop automated solutions to semantically analyse given multimedia contents to assist law enforcement agencies in the preventive policing of online activities. A major challenge for the social media forensic analysis is to preserve the privacy of citizens who use online social networking platforms. This paper presents results of European H2020 project RED-Alert that aims to enable secure and privacy preserving data processing; hence the malicious content and the corresponding personality can be tracked while the privacy of innocent citizens can be preserved. We have mined seven social media channels for content and providing support for ten languages for analysis. Our proposed solution is designed to ensure security and policing of online contents by detecting terrorist material. We have used speech recognition, face and object detection besides audio event detection to extract information from multimedia files. We have applied anonymization techniques to ensure the privacy of citizens using social media. We have discussed the challenges and prospects of this work especially the need of using digital forensic techniques while respecting European and national data protection laws notably GDPR.
The ability to perceptually modify drum recording parameters in a post-recording process would be of great benefit to engineers limited by time or equipment. In this work, a datadriven approach to post-recording modification of the dampening and microphone positioning parameters commonly associated with snare drum capture is proposed. The system consists of a deep encoder that analyzes audio input and predicts optimal parameters of one or more third-party audio effects, which are then used to process the audio and produce the desired transformed output audio. Furthermore, two novel audio effects are specifically developed to take advantage of the multiple parameter learning abilities of the system. Perceptual quality of transformations is assessed through a subjective listening test, and an object evaluation is used to measure system performance. Results demonstrate a capacity to emulate snare dampening; however, attempts were not successful for emulating microphone position changes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.