El Monasterio de Santa Cruz de Coimbra

Since these experiments gathered data for only five songs, the results should be considered as specific rather than general. It is not known at this time how many songs would need to be studied to be able to generalise to mixing as a whole, however, these five songs are considered to be typical within pop/rock styles, due to their conventional instrumentation.

4.6.1 Effect of source position

The final mixes created depended on the initial mix presented, as when beginning with sourceAor

Bthe final mixes are typically closer to this position (see Figs. 4.28, 4.30 and 4.32). This may be an example of an anchoring effect, in which the initially presented stimulus biases an individuals perception of the alternatives. A literature review of this effect is provided by Furnham and Boo [165]. This suggests that music mixing is influenced by the rough mix that is first presented. In mixing experiments care should be taken in choosing the initial conditions. Previous work had used randomised initial conditions [166], although this does make comparison difficult when one is interested in the precise mixing process, as in this chapter. This effect may also have implications in subjective testing of alternate mixes, as that which is presented first may be favoured, or those similar to that which is presented first. Subjective evaluation of alternative mixes is one of the main themes of this thesis and is discussed further in Chapters 6, 7 and 9.

4.6.2 Differences due to reproduction system

King et al. [46] had previously reported a statistically significant difference between the mixes created on headphones and loudspeakers. In that case, the task involved mixing only in one degree-of-freedom (balancing a lead instrument with a backing track). Additionally, that study reported on the 10 participants who took part in both the loudspeaker and headphone sessions and difference in these participants’ mixes. In this chapter, with three degrees-of-freedom (see Figs. 4.12 and 4.13), there was not any statistically significant difference in the levels of the instruments within the mix, when comparing loudspeaker and headphone groups. The small sample size of the headphone group should be noted (n=8), as well as the change in location. However, since the loudspeaker group was tested in a standardised room, this is not thought to be an important factor. It is hypothesised that the main factor explaining the difference between these two studies is the additional complexity and realism of the mixing process presented herein. Additionally, King et al. [46] found the largest inter-group difference for a classical music sample, which is a style of music not represented in this chapter.

4.6.3 Equalisation

The data gathered suggests that, when applying equalisation to a track, it was typical to boost frequencies that are salient in that track, i.e. boosting the low band on bass and kick drum, as shown in Figs. 4.44d and 4.45b. Recall that the crossover to the low band was set to≈180 Hz: this band was generally attenuated for guitar tracks and drum overheads. Vocal EQ application did not appear to follow any particular pattern and has an even spread about the starting position with little observed skewness. These results also suggest that the use of equalisation on the individual channels within a mix does not have a notable effect on the inter-channel loudness differences (see Figs. 4.42 and 4.43). When EQ is applied to a signal, any loudness changes are compensated for by the main track fader.

4.6. DISCUSSION 111

4.6.4 Panning

Many suggest the panning of low-frequency instruments centrally [53, 61, 167, 168]. This pattern of behaviour was observed in these experiments, as kick drum, bass guitar and snare drum were typically panned close to centre. Panning decisions may have been influenced by track ordering, as similar tracks (drum overheads, two guitars) were typically panned opposite to one another as the tracks were read (the fader to the left was panned left and the fader to the right was panned right). No participant defied this convention (by panning the left track right and the right track left). This indicates the importance of GUI elements on music mixing, as in order to pan a pair of similar tracks far apart, their panning faders were moved to a greater visual displacement. The influence of visual information on music mixing is a topic of recent research, for both software [169] and hardware [170] user interfaces. There is evidence of an interaction between panning on level. Panning the guitars far from the centre position, while the vocals remain in the centre, results in a spatial unmasking effect. Consequently, the vocals do not need to be set so loud in order to compete with the guitars. The reduction in vocal level in Fig. 4.13 compared to Fig 4.42 illustrates this.

4.6.5 Importance of vocals

In both mono and stereo experiments, with 4-tracks or 8-tracks, vocals were typically set at the loudest level of all instruments. Additionally, the variance in the panning of vocals was smaller than any other track. Participants chose to place the vocals near the centre of the stereo image. These results highlight the importance of vocals within popular music. The spoken voice has great communicative power, which can be modified by singing. The recorded singing voice therefore has great affective potential and this can be exploited in the mixing process [171].

4.7. CHAPTER SUMMARY 112

4.7

Chapter summary

The work in this chapter introduced the concept of the mix-space and a formulation for track gains, equalisation and panning. The formulation is based on representing the normalised track gains, band gains or pan positions, using hyperspherical coordinates. This parameter space contains all of the mixes that could be created with these tools and forms the basis for the efficient analysis of mixes. In this chapter, mixes were created by test participants in the conventional manner: with individual track faders for gain, 3-band EQ and panning. These mixes were then converted to the mix-domain for comparative analysis. It is perhaps a more simple task to directly generate points in this domain. This topic is explored in Chapter 5 as a means of creating random mixes for Monte Carlo simulation of music mixing and in Chapter 8 as a basis for automated and semi-automated music mixing. There is room for further work. The EQ analysis (“tone-space”) was generated based on a 3-band volume adjustment. While this is generalisable to any number of bands, fur- ther work would be to incorporate more conventional EQ structures, such as parametric EQ. As illustrated in Fig. 4.6 and 4.7, most of this chapter considers a map of the mix-space, rather than the mix-space itself, i.e., the mix-space is a hypersphere in a gain-space but this chapter creates a Euclidean space from the angular components of the hyperspherical coordinates. It is possible to solve these problems directly on the sphere but this would increase the number of dimensions by 1 and circular statistics would be used in place of linear statistics. Some of these issues are explored in greater detail in Chapter 5 and 8.

5

Analysis of randomly-generated mixes

The previous chapter described a series of experiments in which participants used traditional mix- ing interfaces to generate mixes. These were constrained in such a way that their mixes could be then transformed into a simple mix-space, so that they could be compared to one another. Could mixes not simply be generated in the mix-space, directly? It would be advantageous to do so, as asking test participants to generate data is time-consuming and would be unlikely to create a large enough dataset for a robust statistical analysis. The ability to quickly generate a large set of mixes, covering the whole range of mixes that it is possible to make, has a number of uses.

a) Typically, feature-extraction is performed on only one mix of a given song, since only one mix exists. Having a set of alternate mixes for each song allows for a more in-depth testing of the robustness of a feature-extraction algorithm. Rather than gathering a large number of real mixes, which is not always possible, the distribution of features within mixes of a song can be estimated on an artificial dataset of random mixes.

b) Creating a population of mixes for use in optimisation (see Chapter 8).

While Chapter 6 discusses the variation in mixes created by real mix-engineers, a highly infor- mative insight into the process of mix-engineering, it is also necessary to understand the baseline conditions to which these real distributions can be compared. To achieve this, the work presented in this chapter uses randomly generated mixes. These will be compared to the real-world mixes in Chapter 6. The research questions pertaining to this chapter are as follows.

RQ.10 Do mix engineers, collectively, produce mixes with feature distributions similar to randomly generated mixes? If not, how do mixes by real engineers differ from the randomly generated mixes?

RQ.11 Can randomly generated mixes be used to help test the performance and accuracy of feature extraction algorithms, such as onset detection and tempo estimation?

5.1. GENERATING RANDOMISED TRACK GAINS 114