# _Applied_
## 2 Related Work  
ESR has gained more and more attention over the last few years. Most of the recent work focuses on DL approaches to improve the accuracy of the ML models. Some DL models are using the raw waveforms to identify the sound, while others use spectrograms or Mel-Frequency Cepstrum Coefficients (MFCC) as input for the Artificial Neural Networks (ANN) or CNN based classifiers [9; 10; 11]. The authors of [12] compare Supported Vector Machines (SVM) against two CNN models, GoogLeNet and AlexNet, trained on spectrograms from three datasets: ESC-10,ESC-50 and UrbanSound8k. The authors compare not only the accuracy of the models but also their resiliency against adversarial attacks. Adversarial attacks are attacks where the input of the model is altered so that a human still labels the the altered input correctly while the models fail. The authors also propose an approach for increasing the robustness of the SVM model against such types of attacks. A voting system to classify human activity based on sound is used in [13]. The audio is split into frames for which the MFCCs are calculated. These MFCCs are then used by a random forest classifier to predict the human activity on a frame by frame basis. Using a Non-Markovian ensemble voting system these predictions over time are combined to make a final prediction of the present activity. A similar principle is used in [14] where a CNN is used to classify sound clips of 5 s. The input of the CNN are raw audio clips of T-seconds long that are generated using a sliding window. During testing, the outputs of the softmax are summed to classify the test data, which is similar to a voting system. The authors also combined this model with other CNN models that were trained using log mel spectrograms and/or delta log mel spectrograms. Each model was trained individually and the outputs of the models were averaged before applying softmax to classify the sounds. The authors of [8] combined DL and manual feature extraction to classify urban sound. A CNN is used to extract deep features from the spectrograms. These deep features are then combined with manually selected features like zero crossing rate as input to a SVM or Random Forest. Although multiple metrics are used to compare the performance of both classifiers, they are not combined to form one classifier. In [15] the authors combine three different ML techniques with three different sets of features to see which combinations perform the best. However, their definition of best performing only considers accuracy. Although accuracy is an important metric, in real-time applications, the time to perform a prediction is also important. This is especially true when using embedded devices such as Field-Programmable Gate Arrays (FPGAs) or microcontrollers. The authors of [16] do consider using embedded devices. They describe a special function to calculate the cost of embedding the model based on the resources needed and the price of these resources. Three different models based on Gaussian mixture models (GMM), SVM and Deep Neural Networks were considered for classifying sounds from the smoke alarm dataset and the baby cry dataset. The input of the models was a combination of MFCC, spectral centroid, spectral flatness, spectral rolloff, spectral kurtosis and zero crossing rate. The authors do not consider how much time each model needs to classify a sound. A novelty detector for detecting sound events using a GMM model is implemented on a beagle bone in Reference [17]. The authors decided to use a feature similar to MFCC: Power Normalized Cepstral Coefficients (PNCC) together with the first derivative of the PNCC as input for the model. More recently, the authors of [18] compared performance, training and testing time for four different machine learning techniques, SVM, k-Nearest Neighbour (k-NN), bootstrap aggregation and Random Forest, on a Raspberry Pi (RPi) Zero W. To train the models, 3042 audio clips from UrbanSound8k and Sound Events and 8 different classes are used.