Abstract: Head-related transfer functions （HRTFs） refer to the spectral filtering from sound sources to listeners’ eardrums or ear canals. They are a very important cue to spatial hearing localization in 3-D audio simulation due to their ability of extracting sound source location information. Since HRTFs vary as a function of relative sound source locations, frequency, and hearing subjects, and at the same time, there exists a very complicated nonlinear relationship with its independent variables including azimuths, elevations, and frequency, practical implementation of 3-D audio simulation always faces a large set of HRTFs data. A kind of effective HRTFs nonlinear approximation model is presented based on wavelet neural networks to decrease a large set of data. The original HRTFs data can be reset by network training, thus non-individual magnitude HRTFs data at any position are obtained. Audio simulation experiment demonstrates that this HRTFs approximation model not only maintains perceptually relevant features of original HRTFs and but also improves computational efficiency for real-time implementation.