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Audio Content Analysis in The Presence of Overlapped Classes - A Non-Exclusive Segmentation Approach to Mitigate Information Losses. Duraid Y. Mohammed Philip J. Duncan Francis F. Li. School of Computing Science and Engineering, University of Salford UK. Global Summit and Expo on
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Audio Content Analysis in The Presence of Overlapped Classes - A Non-Exclusive Segmentation Approach to Mitigate Information Losses Duraid Y. Mohammed Philip J. Duncan Francis F. Li. School of Computing Science and Engineering, University of Salford UK Global Summit and Expo on Multimedia & Applications August 10-11, 2015 Birmingham, UK
Increasing volume of digital Media archives leading to increased demand for these goals Introduction
Classical classification problems are logically exclusive, i.e. an element is assumed to be a member of one class and of that class only. This hinders some practical uses in audio information mining, since a segment of the soundtrack can have either speech, music, event sounds or a combination of them (fuzzy element) • Non-exclusive classification can mitigate info losses. Classification- Challenge and Solution 13:22
TheConcept Door Knock Hello A system integration approach to audio information mining can be hypothetically built upon the success in the following diverse areas. To re-deploy these tools, it is essential that a pre-processor should effectively Where speech, music and audio events of interest occur. These audio segments can be further processed by dedicated algorithms to obtain further information.
A noise reduction technique. • VAD is employed to detects musical speech and musical segments • Calculate spectral magnitude to musical and musical speech segments. • Estimate the clean speech through the following formula Spectral Subtraction Algorithm
Feature Spaces • Data reduction. • Extract characteristic features. • Mel Frequency Cepstrum Coefficients (MFCCs). • STFT –Temporal pattern analysis. • ZCR, RMS ‘Loudness’, Entropy, Short term energy. • Optimized Feature Space For Speech and Music Detection.
Music Analysis Retrieval and SYnthesis for Audio Signals. • Open source framework for audio processing by George Tzanetakis University of Victoria Canada. • Development of real time audio analysis and synthesis tools • Audio processing system with specific emphasis on MIR. • Implemented for exclusive classification (Speech or Music). • Music genre organisation.
Training Database Building • Speech and Music classes are involved as starting point. • Toward generalization, different styles of samples were included in the training set. • Speech samples (children, male, female, speaker with different languages, aloud speech, speech with laughs,). • Music, all genres are added (Jazz, pop, classical, rock ,…). • All speech and music samples were mixed together after normalizing them to produce speech over music samples.
Summary and Conclusions • Open Structure and Common Interfaces toward general classifier. • Redeployment of currently available techniques. • Encourage third party contributions. • Rapid prototyping of UOA Audio Information Mining system.
