Title :
Combined Supervised and Unsupervised Approaches for Automatic Segmentation of Radiophonic Audio Streams
Author :
Richard, Guilhem ; Ramona, Mathieu ; Essid, Slim
Author_Institution :
GET-ENST, Paris, France
Abstract :
Speech/music discrimination is one of the most studied topics in the domain of audio data segmentation. In this paper, we propose and evaluate a novel method that includes feature selection and a combined supervised and unsupervised strategy for audio streams segmentation. A number of alternatives solutions for each component are assessed and the optimized system is compared to the approaches proposed in the framework of the ESTER campaign.
Keywords :
audio signal processing; feature extraction; ESTER campaign; audio data segmentation; automatic radiophonic audio stream segmentation; feature selection; speech-music discrimination; supervised approaches; unsupervised approaches; Cepstral analysis; Feature extraction; Hidden Markov models; Kernel; Mel frequency cepstral coefficient; Radio broadcasting; Speech; Streaming media; Support vector machine classification; Support vector machines; Speech/Music discrimination; audio segmentation; novelty detection;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0727-3
DOI :
10.1109/ICASSP.2007.366272
