Classification of audio signals using SVM and RBFNN
Expert Systems with Applications: An International Journal
Recognition of Western style musical genres using machine learning techniques
Expert Systems with Applications: An International Journal
A wavelet-based parameterization for speech/music discrimination
Computer Speech and Language
Environmental sound recognition with time-frequency audio features
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Multimedia
IEEE Transactions on Fuzzy Systems
Classification of audio signals using AANN and GMM
Applied Soft Computing
EURASIP Journal on Advances in Signal Processing - Special issue on time-frequency analysis and its applications to multimedia signals
Pattern classification models for classifying and indexing audio signals
Engineering Applications of Artificial Intelligence
Classification accuracy is not enough
Journal of Intelligent Information Systems
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The ongoing advancements in the multimedia technologies drive the need for efficient classification of the audio signals to make the content-based retrieval process more accurate and much easier from huge databases. The challenge of this task lies in an accurate extraction of signal characteristics so as to derive a strong discriminatory feature suitable for classification. In this paper, a time-frequency (TF) approach for audio classification is proposed. Audio signals are nonstationary in nature and TF approach is the best way to analyze them. The audio signals were decomposed using an adaptive TF decomposition algorithm, and the signal decomposition parameter based on octave (scaling) was used to generate a set of 42 features over three frequency bands within the auditory range. These features were analyzed using linear discriminant functions and classified into six music groups (rock, classical, country, jazz, folk and pop). Overall classification accuracies as high as 97.6 % was achieved by linear discriminant analysis of 170 audio signals.