# Automated Personalized Loudness Control for Multi-Track Recordings
## 2 Materials and Methods
### Data Preparation  
As a pre-processing step, all audio files in the dataset were uniformly converted to a standard sampling rate in music of 44.1 kHz [21]. This conversion ensures consistency across all tracks, facilitating a more accurate and controlled analysis of the mixing processes employed by the engineers. The standardization of the sampling rate is a crucial step in maintaining the integrity and comparability of the data, as recommended in the Audio Engineering Society's standard [22]. To avoid unwanted artifacts caused by quantization,we maintained the bit depth at its original value. In order to improve the organization of our dataset, we created a custom Python script to methodically rename audio files, using precise keywords extracted from a comprehensive multi-track database [20]. The process of renaming is not solely administrative; it fulfills a crucial analytical purpose by guaranteeing that tracks of similar categories throughout multiple songs are consistently labeled, hence greatly streamlining data administration and analysis. As an example, the recordings are consistently renamed according to a convention like 'Index_KickIn', 'Index_BassDi', 'Index_Piano', 'Index_LeadVocals_2', 'Index_LeadVocals_3', enabling quick identification and comparison of related parts throughout the collection. Our track list template consists of 66 general tracks conform to the industry standard and is generated through the analysis of several multi-track recordings. It enables a more focused and nuanced examination of the individual contributions of each group to the overall mix, as well as how engineers approach the process of mixing different instrument kinds in different genres:  
* 00-17--Drums and related instruments (e.g., KickIn, SnareUp, HiHat);
* 18-24--Synthesized and electronic percussion (e.g., KickSynth, PercSynthLow);
* 25-30--Bass tracks (e.g., BassDi, BassSynth);
* 31-36--Guitars (e.g., AccGuitarDi, ElGuitarClean);
* 37-45--Keyboards, Piano and synthesized instruments (e.g., ElectricPiano, KeySynthLow);
* 46-58--Orchestral, world instruments and other cinematic instruments (e.g., Woodwinds, Vio1, WorldInstrLow);
* 59-65--Vocals and other (e.g., LeadVox, BackingMed, Other). This methodical approach addresses the lack of a common naming convention among artists and recording engineers, an issue in the profession that we sought to overcome. By using a systematic naming convention, we enhance the organization and accessibility of the dataset. This ensures that tracks are not only uniformly titled, but also classified and arranged in a manner that reflects industry standards. As a post-mixing step, for each sound engineer, the loudness parameters for each track of each song were meticulously extracted and documented, in order to properly represent the preferences of each individual. We created a Python script specifically designed to apply ITU loudness standards [4] for quantifying important loudness measures in every engineer's mix. This script additionally validates the accurate naming of tracks and detects any additional tampering apart from gain adjustment, that might have taken place. The main purpose of this method for loudness extraction was to examine perceptive audio level parameters, offering a quantitative perspective to evaluate the mixing decisions of each engineer. The advantage of this method is represented by its capacity to process a wide range of audio data inputs, guaranteeing consistency in parameter extraction regardless of the file format and duration. Nevertheless, the process was not devoid of obstacles. Novice engineers exhibited inconsistent compliance to protocols, resulting in a deviation from established mixing procedures and causing unanticipated changes in loudness parameters. The extraction algorithm faced considerable obstacles due to inconsistencies in naming standards and unanticipated changes in bit depth, and required human intervention.