# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
###### Abstract  
Sound event detection (SED) plays an important role in understanding the sounds in different environments. Recent studies on standardized datasets have shown the growing interest of the scientific community in the SED problem, however, these did not pay sufficient attention to the detection of artificial and natural sound. In order to tackle this issue, the present article uses different features in combination for detection of machine-generated and natural sounds. In this article, we trained and compared a Stacked Convolutional Recurrent Neural Network (S-CRNN), a Convolutional Recurrent Neural Network (CRNN), and an Artificial Neural Network Classifier (ANN) using the DCASE 2017 Task-3 dataset. Relative spectral-perceptual linear prediction (RASTA-PLP) and Mel-frequency cepstrum (MFCC) features are used as input to the proposed multi-model. The performance of monaural and binaural approaches provided to the classifier as an input is compared. In our proposed S-CRNN model, we classified the sound events in the dataset into two sub-classes. When compared with the baseline model, our obtained results show that the PLP-based ANN classifier improves the individual error rate (ER) for each sound event, e.g., the error rate (ER) is improved to 0.23 for heavy vehicle events and 0.32 for people walking, and minor gains are shown in other events as compared to the baseline. Our proposed CRNN performs well when compare to the baseline and to our proposed ANN model. Moreover, in cross-validation trials, the results in the evaluation stage demonstrate a significant improvement compared to the best performance of DCASE 2017 Task-3, reducing the ER to 0.11 and increasing the F1-score by 10% in the evaluation dataset. Erosion and dilation were used during post-processing. artificial neural network; Mel-frequency cepstrum; multi-model stacked convolutional recurrent neural network; perceptual linear prediction +
Footnote †: journal: Medical  
which include intra-class variability, definitive ambiguity, and overlapping sound events. Recognition of such overlapping sound events is referred as polyphonic SED, as shown in Figure 1.  
SED is the process of jointly identifying the various sound event classes and determining the onset and offset times of each individual sound event instance. In Figure 1, where the two sound event classes are active (time) at the same time, this kind of overlapping can be seen. Polyphonic SED is the term used to describe the SED task in such complex sound scenes with overlapping sound events. The most recent polyphonic SED techniques use deep learning-based methodologies. In the order to train an SED model, datasets that include both audio and the related sound event activity annotation are required. This is shown in Figure 1, which illustrates a general outline for developing SED models for an audio dataset that can extract important audio features. Afterwards, a deep learning technique can be employed to transform these features to a sound event as an output matrix. A number of well-known models [6; 7; 8; 9; 10] have been proposed to classify polyphonic SED tasks. For example, Non-negative Matrix Factorization (NMF) [11] can be used as a pre-processing step to create multiple streams of source-separated audio in order to better deal with overlapping sound events. Similarly, the GMM-HMM model [12] can be used to obtain temporal information on overlapping sound events. In addition, multiple DNN-based approaches to address the challenges of polyphonic SED have been proposed to take advantage of the development of deep learning algorithms. An RNN is a type of DNN designed to represent sequence data, including text, voice, and audio data. The goal of the present study is to create a multi-model that can function effectively with both artificial and natural sounds and to examine how feeding features into the multi-model affects the F1-score and ER.