## 4 Discussion  
Here we provide a proof of concept for the inclusion of \"voice familiarity\" as a potential hearing aid feature. Using a machine learning approach, we demonstrate the feasibility of a convolution neural network (CNN) model to detect and recognize auditory input via conversion to a spectrogram. We also demonstrate that the implemented model, once trained, can accurately classify a family of speakers (i.e., the 10 members of the Modern Family television series) to an accuracy level of >99%. We discuss the implications of the work in the context of hearing aid feature development. In this work, we were concerned with addressing two main goals. Firstly, we set out to determine the feasibility of creating a machine learning classifier to detect and recognize auditory input. After much exploration into the types of neural network models that can be utilized to implement the desired work (i.e., classification of voices), we settled on the CNN model. The rationale for this was the image feature component of the CNN that aligned well with the image feature of audio files, namely the spectrogram. We know that an audio signal, and by transformation, the corresponding spectrogram, carries a staggering amount of information to the listener [43] for a CNN application of emotion recognition from a spectrogram. While alternative neural networks exist (e.g., recurrent neural networks, RNN, which are particularly useful in the prediction of time-series information) [44], the feedforward nature of CNN, in conjunction with the multiple convolutions in the advanced model made this the ideal starting point. Furthermore, while RNN (and hybrid models) can be suitable applications for audio classification and verification, these approaches require substantially more computation power and much larger datasets. According to [PERSON] et al. [29] the hybrid models can be particularly problematic as different sets of parameters/hyperparameters may be selected at each stage of the processes, making the interpretation difficult and possibly not optimal for the final solution. Given our motivation to (eventually) implement the voice familiarity classification to a hearing device, a CNN is an ideal approach as we (1) have maximum control/interpretation over the feature implementations at each step, (2) can train, validate and test the CNN with a relatively small dataset, and (3) do not require egreeiously large computational power. An additional advantage of using the \"image-based\" spectrogram as the input to the CNN model, was the numerous possible features that can be explored, modified, extracted, characterized, etc., in future work. So, for example, if one was interested in determining which feature of the spectrogram \"most\" contributes to voice familiarity, a systematic approach to the modification of each of the components/features of the spectrogram can be taken. There is currently limited information on the various components of the spectrogram that contribute to voice recognition/familiarity, and the model tested here provides an avenue for that work to be explored even further. Secondly, we aimed to test the accuracy with which the CNN model could be trained to classify a series of voices, in this case, the voices from the popular television series Modern Family. In this regard, we considered three pieces of evidence. Firstly, we established that a basic CNN model (i.e., one with a single convolution layer and no normalization) could classify the voices, and indeed the basic CNN model achieved an accuracy of 89%. Secondly, we created and tested a more advanced CNN model (i.e., one with six convolution layers and normalization at each layer that fed forward as the input to the next convolution), which resulted in a \"deeper\" neural network, and subsequently greater classification accuracy (i.e., 99%) as compared to the basic CNN model (i.e., 89%).