Classification of general audio data for content-based retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
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
Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform
Digital Signal Processing
Prediction of Parkinson's disease tremor onset using radial basis function neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Classification of speech dysfluencies with MFCC and LPCC features
Expert Systems with Applications: An International Journal
Perceptive analysis of query by singing system through query excerption
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Audio Classification and Retrieval Using Wavelets and Gaussian Mixture Models
International Journal of Multimedia Data Engineering & Management
International Journal of Systems Biology and Biomedical Technologies
Hi-index | 12.06 |
In the age of digital information, audio data has become an important part in many modern computer applications. Audio classification has been becoming a focus in the research of audio processing and pattern recognition. Automatic audio classification is very useful to audio indexing, content-based audio retrieval and on-line audio distribution, but it is a challenge to extract the most common and salient themes from unstructured raw audio data. 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. Support vector machines are applied to classify audio into their respective classes by learning from training data. Then the proposed method extends the application of neural network (RBFNN) for the classification of audio. RBFNN enables nonlinear transformation followed by linear transformation to achieve a higher dimension in the hidden space. The experiments on different genres of the various categories illustrate the results of classification are significant and effective.