# Adaptive Filtering for Multi-Track Audio Based on Time-Frequency Masking Detection
## 1 Introduction  
With the advent of the internet and advances in electronic and computing technologies, creating music is becoming more accessible than ever before. In one phrase, music production is now available to the masses [1]. It has become much easier for independent artists to access recording tools, software, and learning resources. Therefore, it is also easier for independent musicians to distribute music and gain recognition without a record label [2]. However, independent musicians are also not being drawn into the mainstream as often, making mainstream music formulaic and overly commercialised. Consequently, music is in danger of losing some of the cultural aspects that have previously made it significant [3]. Despite advances in technology, independent musicians still face challenges in achieving a professional-quality product. One of these challenges is multitrack mixing, which involves applying multiple processes (panning, equalisation, compression, gain, etc.) for blending multiple audio tracks in a digital audio workstation (DAW) [4]. DAWs contain a very large library of tools available to perform a mix. For instance, compressors [5], equalisers [6; 7; 8], reverberators [9], dereverberators [10], pitch correction [11], and various other effects [12]. Achieving a professional quality mixing still relies on highly experienced mix engineers, and while DAWs have become more affordable and sophisticated, they are still by no means easy to use for someone without training [13]. There are multiple works that address the challenges of mixing for the untrained user. For instance, using machine learning to predict the values of predefined features that will generate a desired outcome [14; 15] or studying the methodologies of accomplished mixing engineers [16] or their practices in equalisation [17]. One of the key problems in audio multitrack mixing is spectral masking [18]. This is the inability to discern two sounds if they overlap in time and frequency. Highly developed ear skills are required to remove masking in modern DAWs as they still rely on the user to listen and identify which frequencies need adjustment at which times. Several solutions to the challenge of detecting and removing masking have been proposed. These solutions often use cross-adaptive mechanisms to analyse the influence of a track over the othersin order to apply equalisation filters [19]. Features for the analysis, such as intensity at sub-bands [20], or loudness loss [21] are often predefined by the user and these might not be suitable for every piece. Panning has also been used as a means to reduce masking under a similar analysing scheme [22]. These methods might have some setbacks. For instance, relying on the user to predetermine spectral zones or have fixed predefined spectral bands, and might overestimate/underestimate masking due to the omission of the interchangeability of masker and maskee. Equalisation and panning are probably the most effective mechanisms to reduce masking; however, new methods based on artificial intelligence promise not only to reduce masking but to apply all forms of processing in one package [5]. These artificial intelligence methods are still in their infancy and might remove the creative aspects of performing a mix. There are also a number of commercial software tools that aid in achieving a high-quality mix without conventional training. For instance, soundtheory's gullfoss [23] uses equalisation to improve quality, the oeksound's, soothe [24] suppresses prominent frequencies and the Master the mix's fuser [25] compares characteristics in the working mix with a reference track and provides guidance on how to achieve the same characteristics. The developers of such tools might not reveal how their technology works, and without this understanding, it can be challenging for users to apply these tools correctly. At times, this can be exasperated by the use of artificial intelligence approaches [26, 27] where the underlying mechanisms of the algorithms are not clear to the developer either.