# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 2 Previous Work  
In previous research, we explicitly investigated methods to improve foreground speech robustness based on a modular and highly controllable iVector framework. In this framework, Mel-Frequency Cepstral Coefficients (MFCCs) are used as the acoustic input features. In the factor analysis stage, the MFCC feature, which extracts the information in the variable-length recording, is compressed to the iVector with a fixed length. In the classification stage, a regularized Gaussian backend classifier is then used to extract the most likely scene by processing the estimated iVector. This forms the baseline.  
In [15], we proposed the NF-based iVector framework where the MFCCs are derived from an estimate of the background signal power spectral density. We observed significant accuracy improvement in conditions with high signal-to-background ratios (SBRs), but at the cost of poorer performance with low SBRs (The baseline achieved a better performance in such conditions). Here, the term of SBR is utilized to measure the relation between the speech power and the background signal power in a recording. Note that in the speech enhancement literature this is traditionally termed signal-to-noise ratio, where the background/ambient signal is the \"unwanted\" component. However, in ASC, it is exactly this signal that we are interested in, and use of the term 'noise' is therefore inappropriate. In [16], the trade-off was further improved by incorporating speech presence information within the vanilla iVector framework. Three different systems were then proposed: (1) the segment-level weighted score fusion system, an ensemble system which incorporated speech presence information at utterance level to weight the classification scores of the baseline and the NF-based system; (2) the frame-level modified Baum-Welch statistics system, where the speech presence information was included at frame-level; and (3) an ensemble system consisting of the late fusion of a modified Baum-Welch statistics system with a complementarily weighted, modified NF-based system. In these systems, the speech presence possibilities were estimated by GMM-based soft voice activity detection (VAD), which can be holistically incorporated into the iVector framework at either the segment level or the frame level. Experiments showed that, for the iVector framework, sufficient background signal information can be extracted during speech pauses to enable a good scene classification. In addition, the NF-based features yield ASC-relevant information even in the speech-dominant frames. By integrating this information, the iVector framework performance can be further improved. The experiments with the iVector framework were conducted on the DCASE2016 dataset, which contains smaller and less complex data (i.e., fewer scenes, devices, cities, \(\ldots\)). However, when it comes to the DCASE2021 dataset, the performance of the iVector framework was less promising, as the baseline accuracy in the absence of foreground speech was only 47.2%, which is only marginally better than the official baseline (45.6%). This demonstrates that the iVector framework cannot generalize well to larger and more complex datasets, thus it is necessary to introduce deep learning into our ASC systems.  
However, the comparative analysis in our previous work can still provide extra insights into recent popular ASC systems based on deep learning. In this paper, we investigate the methods to improve the foreground speech robustness of the deep learning systems with inspiration from the iVector framework.  
We refer to [15; 16] as complementary reading for this article for a detailed explanation of the iVector framework and noise floor estimate. The chief contributions of these works are summarized in Table 1.