# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 2 Related Work  
Using machine learning for DCASE challenges has substantially accelerated SED research in recent years. Various algorithms have been suggested for the DCASE challenge 2013, including HMM, SVM, and GMM, all based on supervised machine learning. The DNN-based classification of polyphonic SED was modelled in DCASE 2015. The DCASE 2016-2022 competitions have triggered a wave of DNN-based SED approaches [13]. The authors in [1] used a single channel log-mel-band energy (MBE) as the audio feature in the baseline technique for the dataset. The network had two FC layers, the dropout and prediction layers, each with 50 units, with the dropout layer having a 0.2 dropout rate. The number of sigmoid units in prediction layer was equal to the number of classes in the dataset. The Adam optimizer and cross-entropy loss were used for training the network over 200 iterations. In [2], the authors investigated and analyzed the performance of three distinct binaural characteristics, namely, the magnitude and phase components of the short-term Fourier transform, log-mel-band energy, and the extracted log-mel-band energy feature in three different resolution windows. They found that utilizing binaural features produced an error rate comparable to or better than that of single channel features. Using the dataset, it was discovered that the log-mel-band energy feature extracted in various resolution windows produced better best results than the other features. In [14], the authors evaluated mel-filter bank characteristics with identical bank area and height. They applied a DNN structure to all DCASE 2016 [1] tasks, which performed well on all tasks compared to the baseline [15], except for Task 2. They drew the conclusion that DNNs can be successful in many of these tasks, although they may not be always work well, as in the case of DCASE 2016 Task 2. In [16], the authors applied Soft-Median Selection (SMS) to smooth out the features of frames. First, a filter called the Differentiable Soft-Median Filter (DSMF) was created for use with neural networks. Second, the SMS was created by combining the DSMF with a Linear Selection (softmax layer). The suggested DSMF addresses the issue of the gradient algorithm failing to smoothly propagate through the median filter. The authors in [17] used an attention-based capsule network (AttCapsNet) module. They proposed a bidirectional gated recurrent unit (BGRU) module, and a pixel-based attention and bidirectional gated recurrent unit (PBA-AttCapsNet-BGRU) model. Three components of the methods made up the network framework: a CNN audio labelling network, a CNN sound event classification network, and a CRNN feature extraction network. The study in [18] demonstrates how imposing restrictions during training might help in the design of a CRNN network with a clear functional structure which performs better at SED. For the purpose of locating leaks in the pipes in an industrial setting, the authors suggested a multi-stage Machine Learning (ML) pipeline. Using feature selection approaches, they first minimized the dimensionality of data, then added time correlations by extracting time-based feature which were finally fed to a support vector machine (SVM). In [19], the authors employed 40 log-mel filter bank coefficients to process monophonic data taken as an input and to normalize the feature output with zero mean and unit variance. They used a bidirectional RNN as their classifier, with 50 hidden units for the input sequence of consecutive 50 frames, and did not apply any post-processing. Performance on the developmental dataset was assessed, and their proposed approach outperformed the baseline [1]. They came to the conclusion that RNNs are more efficient and adaptable while dealing with varied audio analysis issues. In [20], a binaural I-Vectors Deep Convolutional Neural Networks (DCNN) and the late fusion method were used as a hybrid approach. For Short-Time Fourier Transform (STFT) post-processing, they used a logarithmic filter bank with 24 bands and 2048 sample windows.