## 1 Introduction  
Machine learning techniques, which utilize a lot of data to tackle problems, have improved a lot in recent years. In research topics in acoustic signal processing, machine recognition of sound signals has been of particular researchers' interest [1, 2, 3, 4] to understand a user's situation or find particular events. Such machine-learning-based acoustic signal analysis and classification techniques have been recently studied in various purposes, including anomalous machine sound detection [5], monitoring domestic activity [6], sound event localization and detection [7]. Moreover, the sound classification techniques have been applied in the medical systems, e.g., more sound classification to detect obstructive sleep apnea [8] and bio-inspired voice evaluation [9]. Especially, the machine-based acoustic environment understanding task is related to acoustic scene classification (ASC), which focuses on the recognition of long audio clips, e.g., a 10-second clip, to classify an acoustic environment. Research for ASC tasks started to classify a set of classes, including people, voices, subways, and traffic, with recurrent neural networks and a k-nearest neighbor criterion [10], which was later expanded to a technique using hidden Markov model [11]. Recent techniques for ASC tasks utilize recently developed models based on convolutional neural network (CNN) families [12, 13, 14], such as ResNet [15] and Inception [16]. State-of-the-art ASC models use auxiliary techniques to enhance the performance, e.g., model ensembles and data augmentations. In particular, ASC systems with model ensemble techniques have shown good performance in ASC competitions. In the ASC competition of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Task 1A, 7 models of the top 10 models used ensembles of subsystems. The best models of [PERSON] et al. [12], [PERSON] et al. [17], and [PERSON] and [PERSON] [18], which placed first, second, and third team ranking, consisted of ensembles of three, eight, and three subsystems, respectively. In the ASC task, the ensemble of various model instances seems to be helpful. On the other hand, the ASC and sound event detection (SED) tasks have been expanded to various tasks with specific conditions, e.g., semi-supervised [19], multiple devices, or model-complexity-limited conditions [20]. The semi-supervised SED tasks utilize a dataset that consists of strongly-labeled, weakly-labeled, and unlabeled data to train the classifier, and the ASC with multiple devices utilizes a dataset that consists of data recorded with multiple devices whose frequency characteristics are different from each other. The model-complexity-limited ASC is to construct a small-size model to classify acoustic scenes. Because the small-size ASC model is developed considering mobile devices, there is an upper limit to the model size, e.g., 128 KB. In order to satisfy the complexity constraints, researchers have tried various techniques to reduce the size of large ASC models, including quantization [21], decomposition [22], and pruning [23]. Each algorithm has contributed to the model size reduction in its own way, but the performance aspect was rather complicated [14]. We want to approach the model-complex-limited ASC problem from a different perspective, to develop an auxiliary enhancement method that can be easily applied and that can be applied independently of the existing technique. In order to achieve this goal, we first started with the ensemble technique. As mentioned earlier, the ensemble technique is simple and effective to enhance the performance, but it requires large complexity because it needs several subsystems. For example, if we want to enhance the performance with an ensemble of three subsystems of 128 KB, the entire system requires at least 384 KB. Therefore, the ensemble technique cannot be applied directly to the model-complexity-limited ASC problem, because requiring a large-size model is a serious penalty in the complexity-limited problem.