Brand usage detection via Audio Streams With the constant creation of content on digital media platforms, it is becoming ever more difficult for companies to control the use of their brand. Such use can be found in a review of a competing product, feedback on a news article or merely part of a discussion in a public forum. The reference to the target brand can take the form of a direct textual mention, use of the corporate logo in an image form, or the mentioning of the brand in an audio format. Detection of textual and visual use of brand identity is well researched and considered in an advanced state, yet detection in audio formats is more challenging due to factors such as background noise, voice morphing and heavy accents to name a few. In this initial research, we seek to establish an ideal dataset for further research in this area and an initial review of current solutions for the detection of brand usage in audio streams. Our findings suggest that current solutions do not cater for heavy accents well, which will be the focus of the next stage of this research.

Hierarchical Model for Multimedia Content Classification An automatic content classification technique is an essential tool in multimedia applications. Present research for audiobased classifiers look at short and longterm analysis of signals, using both temporal and spectral features as well as using auditory models. In this paper, we present a hierarchical scheme that maps familiar singletype streaming content sources (e.g., either music, movie, or voice) to processes in a JavaScript Object Notation (JSON) configuration file. For streaming sources containing mixedtype of content (e.g., movies and music) we employ a machine learning (ML) model to classify between the movie, music, and voicebased on metadata. Towards this end, statistical models of the various metadata are created since a large metadata dataset is not available. Subsequently, synthetic metadata are generated from these statistical models, and the synthetic metadata is input to the ML classifier as feature vectors. We demonstrate high discriminating capabilities (accuracy xe2x89x88 90%), with very low latency (viz., xe2x89x88 on an average 7 ms), using real metadata from realworld content such as from YouTubeTM, BlurayTM, and usergenerated content (UGC). The combined hierarchical system (comprising JSON file and MLmodel) reliably identifies the content type with an accuracy greater than 90%.