# Automated Personalized Loudness Control for Multi-Track Recordings
## 3 Results  
Both our methods (genetic algorithm and neural network) were applied for each of the 10 sound engineers, in order to capture the preferences of each individual. For this reason, 10 solutions from the customized genetic algorithm were obtained, as well as 10 trained machine learning models. In order to correctly conduct a comparison of the two methods, we divided the dataset composed of 20 songs in 15 songs used for train and 5 musical pieces for validation purposes, for both algorithms. The 5 mixtures for validation were selected  
Figure 1: Overview of the neural network architecture. The **C** blocks consists in an 1D-convolutional layer and an ReLU activation function. The **L** blocks are represented by a fully connected layer, followed by ReLU function. For each layer the value in the block represents the number of filters (for Conv1D) or the number of neurons (for Linear). The last layer consists the output of the model, which is a vector of 66 coefficients. based on their uniqueness and their differences between other songs. Those validation songs were considered outliers, or in other words, musical pieces too difficult to extract the correct coefficients when combined with other songs. For the training process, it is important to mention that usually songs have multiple versions of the same track (e.g., guitar1, guitar2, violin1, violin2, etc.). When recording or mixing both symphonic and modern music, it is typical to combine similar instrument tracks to make the best use of technical resources and improve artistic expression. For instance, when there are several violinists in an orchestra section or multiple guitar parts in a song, they are frequently combined into a single track during the mixing process. This method saves the channels of the mixing console, decreases the amount of processing required and reduces the size of files, making it easier to handle and save them. From an artistic standpoint, when tracks that perform the same part, such as double tracking guitar riffs or a violin section, are combined, it results in a cohesive and more dense sound. To address this issue, we consider a single track \(x^{k}\) (e.g., guitar) which represents the mean of all the possible versions \(x^{k}_{j}\) of the corresponding track, as presented in Equation (6):  
\[x^{k}_{i}=\frac{1}{V}\sum_{j=0}^{V-1}x^{k}_{i,j}, \tag{6}\]  
where \(j\) represents the index of the current variant from all possible \(V\) versions for track \(k\) of song \(i\). By doing this, we are always able to keep the same number of 66 tracks, no matter the song. However, one constraint is that for both methods, the algorithm will optimize one single coefficient value for the corresponding track, instead of a multitude of them. While all sound engineers had the possibility to change all available tracks (including their variants), the automatic approaches can only approximate the track composed from grouping all available variants. This means that all track versions will have the same mean coefficient at prediction, therefore, it will always be at least a small difference between the predicted mix and its reference. Nevertheless, having the same coefficient value for a group of the same tracks can give a smooth audio sensation and might actually be better perceived by the listener. For the subjective listening test, each engineer participating in the project received four distinct forms of a mix: the RAW, unedited tracks summed after recording; the artist's MIX, which is the engineer's personal mix used as a reference for the algorithms; and the versions generated by the GA and NN algorithms. The engineers were given the songs in a random order and labeled in a manner that prevented them from knowing what version they were reviewing. In order to preserve consistency and assure reliable assessment, each engineer examined the songs in their private studio using their familiar setup that they are used to for mixing.