本研究的目的在發展以軟體為基準的助聽器模擬平台，提供使用者一個輔助工具，透過助聽器模擬平台體驗助聽器功能，幫助使用者選擇合適的助聽器。助聽器模擬平台包含噪音消除、寬動態範圍壓縮(wide dynamic range compression, WDRC)與回饋音消除。本研究主要透過專利文獻來模擬GN ReSound Canta、Sonic Innovation Natura與Oticon Syncro這三家公司的噪音消除方法，使用主觀品質評量(波形圖、聲譜圖、聆聽聲音)與客觀品質評量(分段式訊噪比、對數頻譜失真)來評估噪音消除的效能，並與最佳的修正對數頻譜(optimally-modified log-spectral amplitude, OM-LSA)和可適性β階廣義頻譜刪減(adaptive β-order generalized spectral subtraction(GSS))的噪音消除方法比較，結果顯示三家公司噪音消除方法有效提升訊噪比2 dB以上，而OM-LSA與可適性β-order GSS的噪音消除方法效果雖然比三家公司方法好，但相對的其複雜度也高出三種助聽器公司的方法許多，若是應用在助聽器上不僅增加系統延遲時間，同時伴隨著耗電量增加，這對於需要低延遲時間與低功率消耗的助聽器來說是不合適的。接著我們將三家公司的噪音消除方法與WDRC結合，結果顯示噪音仍能有效的降低。

The purpose of this research is to develop a software-based simulation platform for hearing aid and to provide user an assistive tool. User can experience hearing aid function and choose suitable hearing aid via the hearing aid simulation platform. The platform contains noise reduction, wide dynamic range compression (WDRC) and feedback cancellation. The main purpose of this study is to simulate noise reduction by using patent documents of GN ReSound Canta, Sonic Innovation Natura and Oticon Syncro. We evaluated the noise reduction efficiency by using subjective quality evaluation (waveform, spectrogram, listening) as well as objective quality evaluation (segmental SNR, log-spectral distance) and compared with following noise reduction methods: optimally-modified log-spectral amplitude (OM-LSA) and adaptive β-order generalized spectral subtraction (GSS). The results show that signal-to-noise ratio were improved over 2 dB. Although the noise reduction of OM-LSA and adaptive β-order GSS are better than those other three methods we used. However, the implementation of OM-LSA and adaptive β-order GSS is more complex than that other three noise reduction methods. If we use OM-LSA and adaptive β-order GSS in hearing aid that will increase not only the delay time but also power consumption. This result is against our expectation of low delay time and low power consumption for hearing aid. Finally, we combined three noise reduction methods with the WDRC. The results show that the effects of noise reduction are still similar to those before WDRC.

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