Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection  
[PERSON]  
1 School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea; [EMAIL_ADDRESS] (M.K. ); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea; [EMAIL_ADDRESS] (M.K. ); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea; [EMAIL_ADDRESS] (M.K. ); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
3 Media Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; [EMAIL_ADDRESS] (S.P. ); [EMAIL_ADDRESS] (Y.J.) [PERSON]  
3 Correspondence: [EMAIL_ADDRESS]; Tel. : +82-53-950-552  
spectrum [9; 10; 11]. These are the variations of frequency-domain features used for a characterization of the human hearing system [12]. Mel-frequency-based features have been successfully used in speech signal processing systems employing machine learning techniques. The features exhibit better performance in ASC and SED compared with other existing ones. However, their performance is limited by the less-structured acoustic environmental signals compared with speech signals [5]. Therefore, several alternative features, including a computer-vision-inspired feature [13] and statistical characteristics [14], have been investigated. Recently, several features inspired by the characteristics of the human hearing system, e.g., Mel-frequency discrete wavelet coefficients [15; 16; 17], gammatonegram [18], and gammatone-frequency cepstral coefficients [19], have been investigated. The vast majority of the developed features, including the MFCC and psycho-acoustic-based features, are data-independent feature extraction methods because the feature extraction processes are consistent regardless of the data characteristics in the given problems. Recently, several data-dependent feature extraction methods, including principal component analysis (PCA) [20] and non-negative matrix factorization (NMF) [21], have also been developed. The NMF method has been widely employed to analyze and extract the signal characteristics in the recent acoustic signal processing fields, including music information retrieval [22; 23; 24] and speech signal processing [25; 26; 27]. As the NMF method can extract the common property of the given signals, data-dependent feature extraction methods based on NMF have been developed [5; 28; 29]. The method developed in [5] is a supervised task-driven dictionary learning (TDL) with a limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm [30]. However, feature extraction using the TDL method is strongly related to the classification step. Thus, it is difficult to apply the TDL feature extraction method to other techniques, such as the recently suggested convolutional neural network-based classifiers. Unsupervised NMF-based feature extraction methods were also developed [28; 29]. The method in [29] capitalized on the convolutional NMF and K-means clustering to deal with the uncategorized dataset. The disadvantages of the unsupervised NMF-based feature extraction method are that the data matrix to be handled is too large and the computational cost is quite high. To overcome these disadvantages, [PERSON] and [PERSON] developed a supervised feature extraction method for the monitoring domestic activity problem [31]. The algorithm in [31] exhibited a performance comparable to the state-of-the-art features. However, this study was limited to monitoring domestic activities, which included the activity class recognition problem (without onset/offset information) only. In this paper, we develop and analyze a data-dependent feature extraction method for weakly supervised domestic SED. To achieve this goal, we start with the NMF-based feature extraction method [31] for the monitoring domestic activity problem.