@article{MAKHILLAJIT2010925610,
    title = {Automatic Segmentation and Classification of Audio Broadcast Data},
    journal = {Asian Journal of Information Technology},
    volume = {9},
    number = {2},
    pages = {54-61},
    year = {2010},
    issn = {1682-3915},
    doi = {ajit.2010.54.61},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2010.54.61},
    author = {P.,S. and},
    keywords = {Linear prediction coefficients,linear prediction cepstral coefficients,mel frequency cepstral coefficients,autoassociative neural networks,audio segmentation,classification},
    abstract = {In this study, we describe automatic segmentation and classification methods for audio broadcast data. Today, digital audio applications are part of our everyday lives. Popular examples include audio CDs, MP3 audio players, radio broadcasts, TV or video DVDs, telephones, telephone answering machines and telephone enquiries. Efficient algorithms for segmenting the audio broadcast data and classifying the audio data into predefined categories are proposed. Audio features namely Linear Prediction Coefficients (LPC), Linear prediction cepstral coefficients and Mel Frequency Cepstral Coefficients (MFCC) are used for segmenting and classifying the audio data. Experimental results indicate that the proposed algorithms can produce satisfactory results.}
    }