聲響在生活中扮演重要的角色，若我們可以利用電腦針對居家生活聲響做辨認，就能利用這些資訊了解生活狀態，假設有異常聲響發生，人們可以立即獲得通知。
本論文研究利用樣型識別進行聲響辨認，分為訓練和測試兩部分，訓練過程中分析聲響的聲學特徵並產生特徵向量，搭配聲響種類標籤進而訓練出分類器；測試過程則同樣分析產生特徵向量，再使用分類器分辨未知聲響。過去研究對於單一聲響辨認訓練用的聲響通常只使用對應到欲辨認的單一聲響種類，然而，當應用推向混合聲響辨認時，此方法容易受到混合聲響混淆而導致辨認能力不佳。處理混合聲響辨認的方法可以把各種混合聲響分別視為各種新的單一聲響，其餘流程與單一聲響相同，然而，這種方法會使得聲響種類暴增，辨認過程複雜。本論文根據多標籤分類法(Multi-labeled Classification)的概念，訓練分類器時同時使用單一聲響以及混合聲響，利用支持向量機(Support Vector Machines, SVMs)是二元分類器的特性，以是非題的概念進行辨認，換句話說，系統會對每類單一聲響建立模型，辨認過程就是在檢測未知聲響是否含有這些單一聲響。
本論文比較傳統訓練方法和多標籤訓練方法對於單一聲響、混合聲響的辨認能力，實驗結果顯示：當應用於單一聲響辨認時，傳統方法的Precision較高、多標籤方法則Recall較高；對於處理混合聲響則多標籤方法在各方面之辨認能力都優於傳統方法。

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