在我們生活的環境中，每一種聲音都有其獨特性。我們常常可以藉由環境聲音的特質來辨識出聲音，進而判斷周遭的狀況。例如火災發生時的警報聲。如果能針對這些聲音資訊做分類及辨識，對於瞭解周遭的環境將有很大的幫助。尤其對於聽障者或自動保全系統。此外，聲音資訊在現今的電腦與多媒體應用中，是不可或缺的一部分。有非常大量的資訊是以音訊的檔案格式紀錄下來。運用聲音分類，也有助於搜尋我們想要的聲音片段。
　　本論文中，提出一基於獨立內容分析機與支援向量機與MPEG-7低階聲音描述子之多重聲音環境聲音分類器。我們使用獨立內容分析機來分離多重音源,將分離出來的音源放入聲音辨識系統中辨識,並以spectrum centroid, spectrum spread和spectrum flatness這三個MPEG-7低階聲音描述子作為系統的音訊特徵，提出一結合支援向量機的多重聲音辨識系統。在單一聲音辨識上,我們收集了十五類共六百七十七筆的音訊資料庫。針對此資料庫，我們的分類器最高可以達到89.1%的辨識正確率。

　Different kinds of sound have different properties in our life environment, and we can make out surroundings by recognizing and understanding these properties of environmental sounds. For example, when we hear the fire alarm sound, we can judge there must be fire happening. It will be a great help to us for monitoring surrounding environment if we can classify and identify in accordance with the sound information, especially for the deaf person and security system. Besides, as mentioned in the former article, large amount of information is recorded in files format of audio. Making use of audio classification will be contributive to us for searching the audio segment we want.
　In this thesis, we present a multi-class audio classifier based on independent component analysis (ICA), support vector machine (SVM) and MPEG-7 audio low-level descriptors. We use independent component analysis to separate multi-sound and classify, and we take three MPEG-7 audio low-level descriptors, spectrum centroid, spectrum spread and spectrum flatness, as the features for sound classification and propose a classification method combined SVM and KNN (K-nearest neighbor). We collect an audio database contained 677 wav files of 15 classes. Experiments demonstrate the proposed sound classifier can achieve an 89.3% classification rate.

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