The presently described technology generally relates to digital audio modification. In particular, the presently described technology relates to systems, methods, and apparatus for equalization preference learning for digital audio modification.
In recent decades, audio production tools have increased in performance and decreased in price. These trends have enabled an increasingly broad range of musicians, both professional and amateur, to use these tools to create music. Unfortunately, these tools are often complex and conceptualized in parameters that are unfamiliar to many users. As a result, potential users may be discouraged from using these tools, or may not use them to their fullest capacity.
The parameters provided to users in audio production tools generally reflect the algorithm used to manipulate the sound rather than how manipulating that parameter will influence the way in which that sound is perceived. For example the parameters of an audio equalizer interface might provide the user the ability to increase the gain (in dB) above a particular cutoff frequency (in Hz). However, the perceptual effect of that manipulation might be to make the sound more “bright.” Many users approach an audio production tool with an idea of the perceptual effect that they would like to bring about, but may lack the technical knowledge to understand how to achieve that effect using the interface provided.
Equalizers affect the timbre and audibility of a sound by boosting or cutting the level in restricted regions of the frequency spectrum. These devices are widely used for many applications such as mixing and mastering music recordings. Many equalizers have interfaces that are daunting to inexperienced users. Thus, such users often use language to describe the desired change to an experienced individual (e.g., an audio engineer) who performs the equalization manipulation.
Using language to describe the desired change can be a significant bottleneck if the engineer and the novice do not agree on the meaning of the words used. While investigations of the physical correlates of commonly used adjectives have identified some descriptors for which there is considerable agreement across listeners, they have also identified individual differences. For instance, when using the descriptors “warm” and “clear” to describe the timbre of pipe organs, English speakers from the United Kingdom disagreed with those from the United States on the acoustical correlate.
Further complicating the use of language, the same equalizer adjustment might lead to perception of different descriptors depending on the spectrum of the sound source. For example, a boost to the midrange frequencies might “brighten” a sound with energy concentrated in the low-frequencies (e.g., a bass), but might make a more broadband sound (e.g., a piano) appear “tinny.” Thus, though there have been several recent attempts to directly map equalizer settings to commonly used descriptors, there are several difficulties to this approach.
An alternative approach that circumvents these problems learns a listener's preference on a case-by-case basis. Perhaps the most studied procedure of this type has been developed for setting the equalization curve of a hearing aid. In what is known as a modified simplex procedure, the spectrum is divided into a low- and a high-frequency channel and each combination of low- and high-frequency gains is represented as points on a grid. On each trial, the listener makes two paired preference judgments: one in which the two settings differ in high frequency gain, and one in which they differ in low frequency gain. The subsequent settings are selected to move in the direction of the preference. Once there is a reversal on both axes, the procedure is complete and the gains are set. While this procedure can be relatively quick, the number of potential equalization curves explored is quite small. Although this procedure could theoretically be expanded to include more variables, the amount of time that this would take quickly becomes prohibitively large.