# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 1 Introduction  
Acoustic devices need to be environment-adaptive in order to achieve better user experience. Thus, determining the acoustic environments from acoustic recordings, which is usually termed as acoustic scene classification (ASC), is an advanced research field that many applications can benefit from. For example, for hearing aid devices, different settings/filtering characteristics could be suggested with the changing acoustic scenes based on classifications, like indoor, outdoor, or transportation. Another important application is generating an informative embedding for other algorithms. The background scene information can be embedded in speech enhancement to design tailor-made settings for each scene. Also, for speech separation or speech localisation, the processing can be adapted depending on the types of background scene. The research on ASC systems started with algorithms based on statistical models [1] which summarize the properties of individual audio scenes or whole soundscape categories, typically including Gaussian mixture models (GMMs) [2], hidden Markov models (HMMs) [3], and iVector [4, 5]. GMMs model the statistical distribution of feature vectors associated with different audio scenes. These models represent each acoustic environment as a combination of Gaussian distributions, enabling them to capture diversestatistical properties inherent in audio features. HMMs specialize in capturing the temporal dynamics and represents acoustic environments as sequences of states, modeling transitions between states over time. This temporal aspect, coupled with the statistical insights provided by GMMs, enhances the ability of ASC systems to discern and classify complex soundscapes accurately. ASC systems that fall into this category normally extract the feature vectors from the training data, and a statistical model representing the characteristics of different classes is then trained using the feature vectors. A decision criterion finally assigns the recordings to the best-matched classes. These hand-engineered systems are modular and highly controllable, which makes it easier to analyse the influence of each module. When ASC datasets become larger and more complex [6; 7], deep learning architectures are advantageous. Many DL-based ASC systems are capable of processing large datasets efficiently and show great potential in distinguishing very similar acoustic scenes with high accuracy. Also, they can adapt to complex datasets with recordings from different devices, different geographical locations, and so on. In [8], an overview of DL-based ASC systems is provided. These systems achieved promising results in the Detection and Classification of Acoustic Scenes and Events (DCASE) ([[https://dcase.community/challenge2021/task-acoustic-scene-classification-results-a](https://dcase.community/challenge2021/task-acoustic-scene-classification-results-a)]([https://dcase.community/challenge2021/task-acoustic-scene-classification-results-a](https://dcase.community/challenge2021/task-acoustic-scene-classification-results-a))) (accessed on 7 November 2023), a re-occurring event that stimulates and leads the development of ASC. Most of the systems utilize convolutional neural networks (CNNs) [9; 10] and their extensions. Other architectures include convolutional recurrent neural networks (CRNNs) [11; 12], feedforward neural networks (FNNs) [13; 14], etc. The signal characterising the acoustic scene is the background. When only the background signal is present in the recordings, current ASC systems perform well. However, in real-life applications, a significant amount of foreground speech, irrelevant to the acoustic scenes, is also captured, which leads to performance degradation [15]. In this paper, we are interested in improving the ASC robustness in the presence of foreground speech.