在某些場景，如街道上，會有各種不同的事件同時發生。人類可以透過聽覺，查覺並分辨這些不同事件的能力被稱為聽覺場景分析(Auditory Scene Analysis)。而為了讓電腦能夠做到人類的這項能力的相關研究則是被稱作計算聽覺場景分析(Computational Auditory Scene Analysis,CASA)。聲音事件偵測(Sound Event Detection)是屬於計算聽覺場景分析相關的研究，特別專指將某個場景下的聲音訊號轉換成具體的事件描述的任務。這種技術可能的應用方向涵蓋了居家安全，醫療照顧等。透過樣型識別(Pattern Recognition)的方法，聲音訊號會可透過特徵萃取轉成聲學特徵，再利用機器學習的演算法訓練對應的事件模型。由於資料庫中有多個事件重疊發生的情況，所以需要進行多聲音事件偵測(Polyphonic Sound Event Detection)。相較於每個時間點只需輸出最主要事件的單聲音事件偵測(Monophonic Sound Event Detection)，多聲音事件偵測則更為複雜，可以視為一種多標籤分類(Multi-Label Classification)的問題。這篇論文使用Detection and Classification of Acoustic Scenes and Events 2016 (DCASE 2016)所公開的多聲音事件偵測資料庫(TUT Sound Events 2016)以及其提供的基線系統(Baseline System)。基線系統使用的特徵萃取方法為梅爾倒頻譜係數(Mel Frequency Cepstral Coefficient, MFCC)，多標籤分類方法則是使用高斯混和模型(Gaussian Mixture Model, GMM)。這篇論文則是加入另一種基於人類聽覺的特徵萃取方法以及基於深層式類神經網路(Deep Neural Networks)的多標籤分類方法，嘗試提升系統在錯誤率(Error Rate)以及F分數(F-socre)上的表現。經過不同組合的嘗試，表現最好的方法和基線系統相比在總體錯誤率上降低約0.04，在F分數上提升了約6.6%。

Events occur simultaneously at some scenes. The ability that people are able to detect these events and analyse these scenes by listening is called auditory scene analysis, and the study to let computers have this ability is called computational auditory scene analysis. Sound event detection is a topic that relates to computational auditory scene analysis, which focuses on converting acoustic signal to concrete descriptions of its corresponding sound events. This technology can be used in many applications, such as house security, healthcare, and so on. Through methods developed in pattern recognition, acoustic signal can be turned into feature vectors first, then learning methods can be applied to train models with these feature vectors and their corresponding event labels. Since the data used here were recorded in environments with multiple sound sources, polyphonic sound event detection is required such that the system can detect multiple events at the same time. Compared to monophonic sound event detection, polyphonic sound event detection is more complicated and can be viewed as multi-label classification.
This research used TUT Sound Events 2016 database, which was published by Detection and Classification of Acoustic Scenes and Events 2016 (DCASE 2016). The baseline system of the database used mel frequency cepstral coefficients for feature extraction and Gaussian mixture models for multi-label classification. This paper tries to improve the performance by introducing another feature extraction method based on a human auditory model and multi-label classification methods based on deep neural networks. After trying different combination of feature extraction method and multi-label classification methods, the proposed method reduces the error rate by 0.04 and increases the F-score by 6.6% compared to baseline.

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