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%.

Robust Audio Content Classification Using HybridBased SMD and EntropyBased VAD A robust approach for the application of audio content classification (ACC) is proposed in this paper, especially in variable noiselevel conditions. We know that speech, music, and background noise (also called silence) are usually mixed in the noisy audio signal. Based on the findings, we propose a hierarchical ACC approach consisting of three parts: voice activity detection (VAD), speechmusic discrimination (SMD), and postprocessing. First, entropybased VAD is successfully used to segment input signal into noisy audio and noise even if variablenoise level is happening. The determinations of onedimensional (1D)subband energy information (1DSEI) and 2Dtextural image information (2DTII) are then formed as a hybrid feature set. The hybridbased SMD is achieved because the hybrid feature set is input into the classification of the support vector machine (SVM). Finally, a rulebased postprocessing of segments is utilized to smoothly determine the output of the ACC system. The noisy audio is successfully classified into noise, speech, and music. Experimental results show that the hierarchical ACC system using hybrid featurebased SMD and entropybased VAD is successfully evaluated against three available datasets and is comparable with existing methods even in a variable noiselevel environment. In addition, our test results with the VAD scheme and hybrid features also shows that the proposed architecture increases the performance of audio content discrimination.