{"references": ["Jeongook Song, Hyen-o Oh, Hong-Goo Kong, \"Enhanced long-term\npredictor for Unified Speech and Audio Coding\", Acoustics, Speech and\nSignal Processing (ICASSP), 2011 IEEE International Conference on,\npp: 505-508, 22-27 May 2011.", "Extended Adaptive Multi-Rate-Wideband (AMR-WB+) codec;\nTranscoding functions (Release 9), 3GPP TS 26.304 V6.2.0, 2005-03.", "Martin Dietz, Lars Liljeryd, Kristofer Kj\u00f6rling and Oliver Kunz,\n\"Spectral Band Replication, a novel approach in audio coding\", In 112th\nAES Convention, Munich, May, 2002.", "Nagel, F.; Disch, S., \"A harmonic bandwidth extension method for audio\ncodecs\",Acoustics, Speech and Signal Processing, ICASSP. IEEE\nInternational Conference on, vol., no., pp.145-148, 19-24 April 2009.", "E. Aylon. Automatic detection and classification ofdrum kit sounds.\nMaster's thesis, Universitat PompeuFabra, 2006.", "S. Z. Li, \"Content-based audio classification and retrieval using\nthenearest feature line method,\" IEEE Trans. Speech Audio Process.,\nvol.8, no. 5, pp. 619\u2013625, Sep. 2000.", "ISO/IEC Working Group: MPEG-7 overview. URLhttp://\nwww.chiariglione.org/mpeg/standards/mpeg-7/mpeg-7.htm (2004)\nAccessed8.2.2006.", "Lin, C.-C.; Chen, S.-H.;Truong, T.-K.; Chang, Y., \"Audio Classification\nand Categorization Based on Wavelets and Support Vector Machine\",\nSpeech and Audio Processing, IEEE Transactions on,Volume: 13, Issue:\n5,pp. 644-651, Sept. 2005.", "Eigenfeldt, A., Pasquier, P. 2009. \"Realtime Selection of Percussion\nSamples Through Timbral Similarity in Max/MSP\", in Proceedings of\nICMC. [10] Hyoung-Gook Kim, Commun. Syst. Group, Technische Univ. Berlin,\nGermany Sikora, T.\"Comparison of MPEG-7 audio spectrum projection\nfeatures and MFCC applied to speaker recognition, sound classification\nand audio segmentation\", Acoustics, Speech, and Signal Processing,\n2004. Proceedings. (ICASSP '04). IEEE International Conference on,\nVolume5 pp- 925-8 vol.5, 17-21 May 2004. [11] J. R. Quinlan, \"Learning efficient classification procedures and the\nirapplication to chess end games\", Machin eLearning: An Artificia\nlIntelligence Approach,Vol.1,pp.463-482, Toiga, Palo Alto, CA, 1983. [12] E. Aylon. \"Automatic detection and classification ofdrum kit sounds.\",\nMaster's thesis, Universitat PompeuFabra, 2006.\n[13] Stevens, Stanley Smith; Volkman; John; Newman, Edwin B. \"A scale for\nthe measurement of the psychological magnitude pitch\". Journal of the\nAcoustical Society of America 8 (3): 185\u2013190. 1937."]}

Unified Speech Audio Coding (USAC), the latest MPEG standardization for unified speech and audio coding, uses a speech/audio classification algorithm to distinguish speech and audio segments of the input signal. The quality of the recovered audio can be increased by well-designed orchestra/percussion classification and subsequent processing. However, owing to the shortcoming of the system, introducing an orchestra/percussion classification and modifying subsequent processing can enormously increase the quality of the recovered audio. This paper proposes an orchestra/percussion classification algorithm for the USAC system which only extracts 3 scales of Mel-Frequency Cepstral Coefficients (MFCCs) rather than traditional 13 scales of MFCCs and use Iterative Dichotomiser 3 (ID3) Decision Tree rather than other complex learning method, thus the proposed algorithm has lower computing complexity than most existing algorithms. Considering that frequent changing of attributes may lead to quality loss of the recovered audio signal, this paper also design a modified subsequent process to help the whole classification system reach an accurate rate as high as 97% which is comparable to classical 99%.