Classification of general audio data for content-based retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
AANN: an alternative to GMM for pattern recognition
Neural Networks
Audio-based context recognition
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
A generic audio classification and segmentation approach for multimedia indexing and retrieval
IEEE Transactions on Audio, Speech, and Language Processing
Audio Signal Feature Extraction and Classification Using Local Discriminant Bases
IEEE Transactions on Audio, Speech, and Language Processing
A speech/music discriminator based on RMS and zero-crossings
IEEE Transactions on Multimedia
Multigroup classification of audio signals using time-frequency parameters
IEEE Transactions on Multimedia
A clustering based feature selection method in spectro-temporal domain for speech recognition
Engineering Applications of Artificial Intelligence
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In the age of digital information, audio data has become an important part in many modern computer applications. Audio classification and indexing has been becoming a focus in the research of audio processing and pattern recognition. In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie. For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content. The autoassociative neural network model (AANN) is used to capture the distribution of the acoustic feature vectors. Then the proposed method uses a Gaussian mixture model (GMM)-based classifier where the feature vectors from each class were used to train the GMM models for those classes. During testing, the likelihood of a test sample belonging to each model is computed and the sample is assigned to the class whose model produces the highest likelihood. Audio clip extraction, feature extraction, creation of index, and retrieval of the query clip are the major issues in automatic audio indexing and retrieval. A method for indexing the classified audio using LPCC features and k-means clustering algorithm is proposed.