Mustafa Sert

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This paper describes the development of a generated solution for classification and segmentation of broadcast news audio. A sound stream is segmented by classifying each sub-segment into silence, pure speech, music, environmental sound, speech over music, and speech over environmental sound classes in multiple steps. Support Vector Machines and Hidden(More)
We present a novel algorithm for structural analysis of audio to detect repetitive patterns that are suitable for content-based audio information retrieval systems, since repetitive patterns can provide valuable information about the content of audio, such as a chorus or a concept. The Audio Spectrum Flatness (ASF) feature of the MPEG-7 standard, although(More)
A typical content-based audio management system deals with three aspects namely audio segmentation and classification, audio analysis, and content-based retrieval of audio. In this paper, we integrate the three aspects of content-based audio management into a single framework and propose an efficient method for flexible querying and browsing of auditory(More)
—Deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition. In this study, we investigate various aspects of convolutional neural networks (CNNs) from the big data perspective. We analyze recent studies and different network architectures(More)
—Automatic recognition of human emotional states is an important task for efficient human-machine communication. Most of existing works focus on the recognition of emotional states using audio signals alone, visual signals alone, or both. Here we propose empirical methods for feature extraction and classifier optimization that consider the temporal aspects(More)