TY  - JOUR
T1  - Automatic Segmentation and Classification of Audio Broadcast Data
AU - Dhanalakshmi, P. AU - Palanivel, S. AU - Ramalingam, V. 
JO  - Asian Journal of Information Technology
VL  - 9
IS  - 2
SP  - 54
EP  - 61
PY  - 2010
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2010.54.61
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2010.54.61
KW  - Linear prediction coefficients
KW  -linear prediction cepstral coefficients
KW  -mel frequency cepstral coefficients
KW  -autoassociative neural networks
KW  -audio segmentation
KW  -classification
AB  - 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.
ER  -