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題 名 | A Study of the Application of an Average Energy Entropy Method for the Endpoint Extraction of Frog Croak Syllables=平均能量熵值法應用於蛙鳴音節端點萃取之研究 |
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作 者 | 謝勝治; 陳文平; 林文智; 周富三; 賴俊如; | 書刊名 | 臺灣林業科學 |
卷 期 | 27:2 2012.06[民101.06] |
頁 次 | 頁177-189 |
分類號 | 388.691 |
關鍵詞 | 能量端點偵測法; 熵值端點偵測法; 聲紋辨識; Energy-based endpoint detection; Entropy-based endpoint detection; Voice-print recognition; |
語 文 | 英文(English) |
中文摘要 | 能量(energy)端點偵測法經常被用於擷取信號的語音片段之時域(time domain)分析,以節省計算量,但此法容易受到雜訊影響而擷取不正確的語音片段,這對於分析野外所錄製之音檔而言,辨識能力將大受影響;而熵值(entropy)端點偵測法雖有較佳的抗噪能力,但背景雜訊不穩定的頻譜分佈,會導致非有聲段部份的熵值起伏劇烈而影響端點的偵測。因此本文提出平均能量熵值端點偵測法(averageenergy entropy(AEE)endpoint detection)來改善上述問題,並與能量、越零率、熵值等三種端點偵測法做比較,而在蛙鳴聲紋辨識實驗上,經實驗18種野外蛙類音檔分析後發現,平均能量熵值端點偵測法有最佳的端點萃取能力,而搭配線性預估倒頻譜係數與動態時軸校正演算法則有最佳的辨識能力。 |
英文摘要 | Energy-based endpoint detection is commonly used in time domain analyses of speech segments of extracted signals to reduce the amount of computation required. However, this approach may extract incorrect speech segments due to interference by noise, which can significantly impair its recognition ability when analyzing sound files recorded in the wild. In contrast, entropy-based endpoint detection performs better in terms of noise suppression. Unfortunately, background noise that has a non-stationary frequency distribution causes drastic fluctuations in entropy values of silent segments, and weakens endpoint detection. This paper proposes using average energy entropy (AEE) endpoint detection to address these issues, and compares the AEE method with 3 other endpoint detection methods-energy-based, zero-crossing rate, and entropy-based detection methods. In experiments on frog voice-print recognition, 18 types of frog croaks recorded from the wild were analyzed, and the results revealed that the AEE method had the optimal endpoint extraction capability; and when used in concert with the linear predicative cepstral coefficients, Mel-frequency cepstrum coefficients with dynamic time warping algorithm, the AEE capability for recognition was optimized. |
本系統中英文摘要資訊取自各篇刊載內容。