# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 1 Introduction
To our best knowledge, although plenty of research focuses on improving ASC systems in terms of learning architectures and training paradigms, there is very little research on how to improve foreground speech robustness--as we also note in our previous work within the iVector framework [15; 16]. A rather naive way would be to remove foreground speech by repurposing existing speech denoising frameworks (e.g., [17]). For example, in [18], removing foreground speech is included as a pre-stage of the ASC framework. Such methods might affect the ASC system by introducing (non-linear) artefacts into the processed signal. Instead, in our previous work, we showed that utilising noise-floor (NF)-based features [19; 20; 21] and incorporating speech presence information could improve the foreground speech robustness of the classical iVector-based ASC. It remains to be explored whether DL-based systems could share the benefits of our previous research or whether they can be made inherently robust by training. In this paper, a residual network (ResNet) architecture [22] is taken as the baseline, through which we hope to extend the conclusions also to other deep learning models. This ResNet model, as our entry to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2021 challenge, achieved an accuracy of 68.8% in the evaluation dataset, while the official baseline accuracy was 45.6%. Meanwhile, the performance of the best system in the challenge was 76.1%. ResNet models with different complexity are explored in order to show the generalisation of the proposed methods. We believe the results will extend also to other DL-based frameworks since in this paper we are mainly focusing on optimal input features and ensemble methods. The noise floor estimate as an input stream in the ASC system is first investigated. The noise floor is obtained by statistical methods and typically yields a highly smoothed estimate of the background signal spectrum. While this feature is less corrupted by the more dynamically varying speech spectrum, the smoothing nevertheless results in loss of information and, subsequent sub-optimal performance of the classifier. To incorporate more signal characteristics in the background dominant frames, therefore, we next consider emphasising these frames by incorporating the speech presence information. For this, a state-of-the-art speech enhancement (SE) system, CRUSE [23], is integrated to provide the short-time Fourier domain speech mask. This mask can be regarded as the speech presence probability. Finally, for a global decision, ensemble methods including model voting and meta-learner are investigated to achieve the best performance. The remainder of this paper is organized as follows. In Section 2, we summarize our previous work within the iVector framework, which provides crucial cues to the strategies for deep learning systems. Section 3 presents the ResNet model that is utilized as the baseline, including the model architecture, feature extraction, and training paradigm. Section 4 includes a description of the proposed modifications: use of the noise floor features and the speech presence probability. Different combinations using ensemble methods are introduced for global decisions. Section 5 describes the experimental setup and evaluation results including the different combinations using ensemble methods. Section 6 concludes the paper.