# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 3 Methodology
### Dataset  
For the training of the model, we compile a dataset of 800 semantically-annotated equaliser parameter space settings, comprising 40 participants equalising 10 musical instrument samples using two descriptive terms: _warm_ and _bright_. To do this, participants were presented with the musical instrument samples in a DAW and asked to use a parametric equaliser to achieve the two timbral settings. After each setting was recorded, the data were recorded and the equaliser was reset to unity gain. During the test, samples were presented to the participants in a random order across separate  
Figure 1: An overview of the Semantic Audio Feature Extraction (SAFE) equaliser and its feature extraction architecture. (**a**) The extended Semantic Audio Equalisation plug-in with the two-dimensional interface. To modify the _brightness_/_varmth_ of an audio signal, a point is positioned in two-dimensional space; (**b**) The SAFE feature extraction process, where \(A\) represents the audio features captured before the effect is applied, \(A^{\prime}\) represents the features captured after the effect is applied, and \(P\) represents the parameter vector.  
DAW channels. Furthermore, the musical instrument samples were all performed unaccompanied, were Root Mean Square (RMS) normalised and ranged from 20 to 30 s in length. All of the participants had normal hearing, were aged 18-40, and all had at least 3 years' music production experience.  
The descriptive terms (_warm_ and _bright_) were selected for a number of reasons; firstly, the agreement levels exhibited by participants tend to be high (as suggested by [3]), meaning there should be less intra-class variance when subjectively assigning parameter settings. When measured using an agreement metric, defined by [13] as the log number of terms over the trace of the covariance matrix, _warm_ and _bright_ were the two highest ranked terms in a dataset of 210 unique adjectives. Secondly, the two terms are deemed to be sufficiently different enough to form an audible timbral variation in low dimensional space. While the two terms do not necessarily exhibit orthogonality (for example, _brightness_ can be modified with constant _warmth_[9]), they have relatively dissimilar timbral profiles, with _brightness_ widely accepted to be highly correlated with the signal's spectral centroid, and _warmth_ often attributed to the ratio of the first three harmonics to the remaining harmonic partials in the magnitude spectrum [22].  
The parameter settings were collected using a modified build of the SAFE data collection architecture, in which descriptive terms, audio feature data, parameter data, and metadata can be collected remotely within the DAW environment and uploaded to a server. As illustrated in Figure 1b, the SAFE architecture allows for the capture of audio feature data before and after processing has been applied. Similarly, the interface parameters \(P\) are captured and stored in a linked database. For the purpose of this experiment, the architecture was modified by adding the functionality to capture LTAS coefficients, with a window size of 1024 samples and a hop size of 256.  
While the SAFE project comprises a number of DAW plug-ins, we focus solely on the parametric equaliser, which utilises five biquad filters arranged in series, consisting of a low-shelving filter (LS), three peaking filters (\(Pf_{n}\)), and a high-shelving filter (HS), where the LS and HS filters each have two parameters and the (\(Pf_{n}\)) filters each have three, as described in Table 1.