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Recognizing human actions in realistic scenes has emerged as a challenging topic due to various aspects such as dynamic backgrounds. In this paper, we present a novel approach to taking audio context into account for better action recognition performance, since audio can provide strong evidence to certain actions such as phone-ringing to answer-phone. At first, classifiers are established for visual...
In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector produced by compressing the local features. Two variants of naive Bayes classifiers are studied based on the extensions of standard nearest neighbor...
Audio tags describe different types of musical information such as genre, mood, and instrument. This paper aims to automatically annotate audio clips with tags and retrieve relevant clips from a music database by tags. Given an audio clip, we divide it into several homogeneous segments by using an audio novelty curve, and then extract audio features from each segment with respect to various musical...
This paper proposes a SVM-based method to deal with the problem of detecting audio events(cheering and applause) by audio analysis. In our framework, a sliding window is first used to pre-segment the audio stream into short segments by moving from start to the end. Second, various kinds of audio features are extracted to represent different audio sounds in each segment. Third, SVM(super vector machine)...
One of the major challenges in classification problems based on signal decomposition approach is to identify the right basis function and its derivatives that can provide optimal features to distinguish the classes. Local discriminant bases (LDB) algorithm is one such algorithm, which efficiently selects a set of significant basis functions from the library of orthonormal bases based on certain defined...
Video scene classification and segmentation are fundamental steps for multimedia retrieval, indexing and browsing. In this paper, a robust scene classification and segmentation approach based on support vector machine (SVM) is presented, which extracts both audio and visual features and analyzes their inter-relations to identify and classify video scenes. Our system works on content from a diverse...
Video scene classification and segmentation are fundamental steps for multimedia retrieval, indexing and browsing. In this paper, a robust scene classification and segmentation approach based on support vector machine (SVM) is presented, which extracts both audio and visual features and analyzes their inter-relations to identify and classify video scenes. Our system works on content from a diverse...
In this paper, we investigate the noise robustness of Wang and Shamma's early auditory (EA) model for the calculation of an auditory spectrum in audio classification applications. First, a stochastic analysis is conducted wherein an approximate expression of the auditory spectrum is derived to justify the noise-suppression property of the EA model. Second, we present an efficient fast Fourier transform...
Audio classification is an important issue in current audio processing and content analysis researches. Speech/music classification is one of the most interesting branches of audio signal classification. In this paper we present an unsupervised clustering method, based on one-class support vector machines (OCSVM) and inspired by the classical K-means algorithm, which effectively classifies speech/music...
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