caa a22 4500 60544790X CHVBK 20210128100134.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s11042-014-1859-8 doi (NATIONALLICENCE)springer-10.1007/s11042-014-1859-8 Efficient implementation techniques of an SVM-based speech/music classifier in SMV [Elektronische Daten] [Chungsoo Lim, Joon-Hyuk Chang] For real-time speech and audio encoders used in various multimedia applications, low-complexity encoding algorithms are required. Indeed, accurate classification of input signals is the key prerequisite for variable bit rate encoding, which has been introduced in order to effectively utilize limited communication bandwidth. This paper investigates implementation issues with a support vector machine (SVM)-based speech/music classifier in the selectable mode vocoder (SMV) framework, which is a standard codec adopted by the Third-Generation Partnership Project 2 (3GPP2). While a support vector machine is well known for its superior classification capability, it is accompanied by a high computational cost. In order to achieve a more realizable system, we propose two techniques for the SVM-based speech/music classifier, aimed at reducing the number of classification requests to the classifier. The first technique introduces a simpler classifier that processes some of the input frames instead of the SVM-based classifier, and the second technique skips a portion of input frames based on strong inter-frame correlation in speech and music frames. Our experimental results show that the proposed techniques can reduce the computational cost of the SVM-based classifier by 95.4 % with negligible performance degradation, making it plausible for integration into the SMV codec. Springer Science+Business Media New York, 2014 Speech/music classification nationallicence Support vector machine nationallicence Selectable mode vocoder nationallicence Embedded system nationallicence Lim Chungsoo Korea National University of Transportation, 50 Daehak-ro, Chungbuk, Choungju-si, Republic of Korea aut Chang Joon-Hyuk Hanyang University, 222 Wangsimni-ro, Seoul, Seongdong, Republic of Korea aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 5375-5400 1380-7501 74:15<5375 2015 74 11042 https://doi.org/10.1007/s11042-014-1859-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-1859-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lim Chungsoo Korea National University of Transportation, 50 Daehak-ro, Chungbuk, Choungju-si, Republic of Korea aut NATIONALLICENCE 700 1- Chang Joon-Hyuk Hanyang University, 222 Wangsimni-ro, Seoul, Seongdong, Republic of Korea aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/15(2015-08-01), 5375-5400 1380-7501 74:15<5375 2015 74 11042