Mostly the real life signals are time varying in nature. For proper characterization of such signals, time-frequency representation is required. The STFT (short-time Fourier transform) is a classical tool used for this purpose. The limitation of the STFT is its fixed time-frequency resolution. Thus, an enhanced version of the STFT, which is based on the cross-level sampling, is devised. It can adapt the sampling frequency and the window function length by following the input signal local variations. Therefore, it provides an adaptive resolution time-frequency representation of the input. The computational complexity of the proposed STFT is deduced and compared to the classical one. The results show a significant gain of the computational efficiency and hence of the processing power. The processing error of the proposed technique is also discussed.<\/p>\r\n","references":"[1] J.W. Mark and T.D. Todd, \"A nonuniform sampling approach to data\r\ncompression\", IEEE Transactions on Communications, vol. COM-29, pp. 24-32, January 1981.\r\n[2] E. Allier, G. Sicard, L. Fesquet and M. Renaudin, \"A new class of asynchronous\r\nA\/D converters based on time quantization\", ASYNC'03,\r\npp.197-205, May 2003.\r\n[3] F. Aeschlimann, E. Allier, L. Fesquet and M. Renaudin, \"Asynchronus\r\nFIR filters, towards a new digital processing chain\", ASYNC'04, pp. 198-206, April 2004.\r\n[4] N. Sayiner et al. \"A Level-Crossing Sampling Scheme for A\/D Conversion\",\r\nIEEE Transactions on Circuits and Systems II, vol. 43, pp. 335-339, April 1996.\r\n[5] S.C. Sekhar and T.V. Sreenivas, \"Adaptive window zero-crossing based instantaneous frequency estimation\", EURASIP Journal on Applied Signal Processing, pp.1791-1806, Issue 1, January 2004.\r\n[6] D. Gabor, \"Theory of communication\", Journal of the IEE, Vol.93(3), pp. 429-457, 1946.\r\n[7] R. Polikar, \"The engineer-s ultimate guide to wavelet analysis\", Rowan University, College of Engineering, retrieved June, 2006.\r\n[8] S. M. Qaisar et al. \"Spectral Analysis of a signal Driven Sampling\r\nScheme\", EUSIPCO-06, September 2006.\r\n[9] S. de Waele and P.M.T.Broersen, \"Time domain error measures for\r\nresampled irregular data\", IEEE Transactions on Instrumentation and\r\nMeasurements, pp.751-756, May 1999.\r\n[10] S. de Waele and P.M.T.Broersen, \"Error measures for resampled ir\u00acregu-lar data\", IEEE Transactions on Instrumentation and Measure\u00acments, pp.216-222, April 2000.\r\n[11] S. M. Qaisar et al. \"Computationally efficient adaptive rate sampling and filtering\", EUSIPC0'07, pp.2139-2143, September 2007.\r\n[12] M. Gretains, \"Time-frequency representation based chirp like signal analysis using multiple level crossings\", EUSIPC0'07, pp.2154-2158, Sep-tember 2007.\r\n[13] S. M. Qaisar et al. \"Adaptive rate filtering for a signal driven sampling scheme\", ICASSP'07, pp.1465-1468, April 2007.\r\n[14] M. Vetterli et al. \"Wavelets and filter banks: Theory and design\", IEEE Transections on signal processing, Vol.40, pp.2207-2232, 1992.\r\n[15] F. Harris, \"Multirate signal processing in communication systems\", EUSIPC0'07, September 2007.\r\n[16] K. M. Guan and A.C. Singer, \"Oppertunistic Sampling by Level-Crossing\", ICASSP'07, pp.1513-1516, April 2007.\r\n[17] F. Akopyan et al. \"A level-crossing flash analog-to-digital converter\", ASYNC'06, pp.12-22, Grenoble, France, March 2006.\r\n","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 17, 2008"}