## 4 Current Research and Future Directions
### Intelligent Music Production  
The use of audio effects has been extended to automating tools for Intelligent Music Production (IMP) and automatic mixing [152]. Within this field, there has been a large focus on the use of some signal analysis approaches to _automate_, or directly control, some parameters of a preexisting audio effect. This can be performed by machine learning from data [153; 154], performing some curve fitting or mapping to some higher-level parameters [155; 156] or using some direct signal analysis to control an audio effect parameter directly [157; 158].  
Figure 9: Signal flow of an adaptive audio effect using control parameters obtained from metadata.  
[PERSON] et al. [152] present one approach to the use of audio effects for the application of automatic mixing, where there is some high level analysis of the audio signals to capture some audio feature representation, which can then be applied directly to a mixing process in a highly deterministic way. This concept is presented more formally by [PERSON] et al. [159], who combine the Audio Effects Ontology [160] and the Audio Feature Ontology [161]. This concept is demonstrated in the Semantic Compressor [150]. The great advantage of the approach presented by [PERSON] et al. [159] is that it allows for a constraint optimisation to be applied, where a number of contradictory rules may be considered and interpreted to allow for one of many suitable answers to be identified. As discussed in [PERSON] and [PERSON] [162], the use of a single audio effect for a single purpose is not always applicable in the field of IMP. There is single use for an audio effect, and as such, there is little scope for fully automating a single audio effect, deterministically, through direct audio feature analysis. The automation of audio effect parameters for automatic mixing is only one approach for performing IMP [163].  
[PERSON] et al. [164] proposed the integration of source separation techniques into the field of IMP, which would allow for a higher level of understanding as to the processing taking place. In a mixing context, the engineers' task is to combine a number of sources in a pleasant and appropriate manner, regardless of how many microphones are used to capture the signal. This work was prototyped, which demonstrated that source separation will improve IMP systems [157].  
There is growing scope for the use of Deep Neural Network (DNN) approaches in the use of automatic mixing. [PERSON] and [PERSON] [165] identify that previous automatic mixing approaches do not capture the highly nonlinear approaches. Since then, there have been several approaches towards using DNN for automatic mastering, including source separation and remixing [166], automatic mixing through audio feature transformation [167; 168], or some audio mixing style transfer approach [169]. Many of these approaches are heavily derived from the field of image processing, particularly the field of audio style transfer. This is yet another example of transsectorial innovation being present at all stages of audio effect production. DNNs are also at the centre of current research in modelling audio effects, for both linear transformations such as equalization as well as time-varying audio effects involving delay lines and low-frequency oscillators [170; 171; 172].