Abstract
Many technical papers have been published related to music identification. However, most of these papers have focused on describing their algorithms and their overall performance. When music identification is applied to embedded devices, the performance is affected by the level of frame boundary desynchronization, environmental noise, and channel noise. This paper presents an empirical performance analysis of music identification, in terms of its Peak Point Hit Ratio (PPHR). In theory, music identification systems guarantee a 100 % accurate PPHR between the queried music and its reference. However, PPHR falls to 40.8 % by desynchronization when a frame boundary is desynchronized by half the frame shift. In addition, due to environmental noise, PPHR decreases to 69.6, 59.4, 46.1, and 24.3 % at SNR 15 dB, 10 dB, 5 dB, and 0 dB, respectively. For music clips recorded in an office environment, PPHR is 58.7 % due to environmental and channel noise.
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Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MEST) (2010-0004522).
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Cho, N. et al. (2013). Performance Analysis of Noise Robust Audio Hashing in Music Identification for Entertainment Robot. In: Park, J., Barolli, L., Xhafa, F., Jeong, HY. (eds) Information Technology Convergence. Lecture Notes in Electrical Engineering, vol 253. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6996-0_103
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DOI: https://doi.org/10.1007/978-94-007-6996-0_103
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