## 1 Introduction  
Environmental sounds, which are an integral part of multimedia data, contain plenty of information, such as location and activities. To efficiently utilize the audio information for indexing massive multimedia contents, many research efforts have been spent on developing acoustic scene classification (ASC) system using advanced signal processing and machine learning techniques in recent years [1; 2; 3]. Although some progress has been made, the key issues in acoustic scene understanding, i.e., acoustic feature representations development and efficient framework, are still open questions to the research field. Psychological research findings reveal that \"auditory system summarizes the temporal details of sounds using time-averaged statistics to understand acoustic scenes\" [4; 5]. Inspired by the results,plenty of works endeavour to adopt descriptive statistics to characterize textures in an acoustic scene for content-based classification [2, 6]. Standard approaches to ASC firstly converts input audio signal to a time-frequency representations (TFRs) by using either handcrafted features, such as Mel-scale spectrogram and mel-frequency cepstral coefficients (MFCCs), or learning-based feature, e.g., unsupervised feature learning with neural networks. Then, statistical moments, e.g., mean, variance, skewness and kurtosis, are subsequently employed to convert TFRs (matrix) to compact feature vector [7, 8, 9] for further statistical classification using supervised learning. Figure 1 shows a general flowchart of ASC. Although descriptive statistics are off-the-shelf tools which can be used for acoustic scene understanding, the \"summarization\" performed by the auditory system is essentially different from arithmetic averaging. Descriptive statistics of time-averaging implies that every frame of acoustic feature carries similar information for ASC. In contrast, the auditory system adopts an adaptive scheme that \"_keeping the environment-specific acoustic information, while weeding out the irrelevant detail_\", according to the neuroscience research result [10]. In other words, auditory system has a _content-enhancing_ function that draws more attention on discriminative patterns in acoustic scenes, which is well-suited to acoustic scenes parsing. This paper attempts to mimic _discriminantcontent - enhancing function_ of auditory perception for ASC by using data-driven statistical machine learning. We begin this study with the primary mathematical formulation, that is, we assume ambient sound can be decomposed into two categories, which can be expressed as follows:  
\[s(t)=s_{r}(t)+s_{i}(t). \tag{1}\]  
Concretely in ASC tasks, \(s_{r}(t)\) and \(s_{i}(t)\) denote scene-relevant/irrelevant sounds, respectively. The first-category usually exhibits high temporal homogeneity, and thus delivers predominant discriminative information of acoustic scenes. Furthermore, it can be described as a superposition of many similar scene-relevant acoustic events over background textures [4], which have been extensively investigated in psychology research of human auditory perception [7, 11]. The latter component \(s_{i}(t)\) denotes scene-irrelevant sound which hardly contributes to ASC. For instance, speech can occur in different acoustic scenes, such as in street, shop, and cafe. It is noteworthy that \(s_{i}(t)\) usually presents complex spectro-temporal patterns and stronger energy, and thus can severely affect ASC performance. Therefore, these outliers to current acoustic scene category should be handled carefully at feature  
Figure 1: General framework of acoustic scene classification system. ASC can be performed by examining homogeneous structures of audio data. extraction stage. By exploiting characteristics of the two components, we found that \(s_{r}(t)\) commonly present stationary statistical properties over time; in contrast, scene-irrelevant sounds, which exhibit complex spectro-temporal patterns in short-time periods, are discretely superimposed.