Intelligent Digital Mixing Assistants (IMAs) are being used with increasing frequency in music mixing. Utilising various signal analysis techniques, assistants can detect, manage, or help resolve a variety of sound engineering-related issues. While taking advantage of intelligent functionality, creative users must communicate with IMAs to select from among the types of assistance and auto-corrections offered. Developments in human-automation interactions (HAIs) in other domains have shown that user-tool communications can be rich and dynamic when the tool encourages the reciprocation of knowledge and when user and tool are able to learn from and adapt to the other – in this case – creative possibilities and actions. With the aim of better aligning HAI with the actual work of mixing, this study investigates the communication requirements for an intelligently assisted co-creative environment. These insights are derived from existing literature on the cognition of creativity relevant to mixing, from HAI in other areas of application, and from cognitive systems engineering generally. Findings point to a set of design goals, interaction parameters and constraints for an intelligent digital collaborator.

Intelligent Mixing Systems (IMS) are rapidly becoming integrated into music mixing and production workflows. The intelligences of a human mixer and IMS can be distinguished by their abilities to comprehend, assess, and appreciate context. Humans will factor context into decisions, particularly concerning the use and application of technologies. The utility of an IMS depends on both its affordances and the situation in which it is to be used. The appropriate use for conventional purposes, or its utility for misappropriation, is determined by the context. This study considers how context impacts mixing decisions and the use of technology, focusing on how the mixer?s understanding of context can inform the use of IMS, and how the use of IMS can aid in informing a mixer of different contexts.

Music producers and casual users often seek to replicate dynamic range compression used in a particular recording or production context for their own track. However, not knowing the parameter settings used to produce the audio using the effect may become an impediment, especially for beginners or untrained users who may lack critical listening skills. We address this issue by presenting an automatic compressor plugin relying on a neural network to extract relevant features from a reference signal and estimate compression parameters. The plugin automatically adjusts its parameters to match the input signal with a reference audio recording as closely as possible. Quantitative and qualitative usability evaluation of the plugin was conducted with amateur, pro-amateur, and professional music producers. The results established acceptance of the core idea behind the proposed control method across these user groups.

Intelligent Mixing Systems (IMS) are being integrated into mixing workflows, however, there is little discussion around how these technologies are impacting mixing practices. This study explores th ...

It is not uncommon to hear musicians and audio engineers speak of warmth and brightness when describing analog technologies such as vintage mixing consoles, multitrack tape machines, and valve compressors. What is perhaps less common, is hearing this term used in association with retro digital technology. A question exists as to how much the low bit rate and low-grade conversion quality contribute to the overall brightness or warmth of a sound when processed with audio effects simulating early sampling technologies. These two dimensions of timbre are notoriously difficult to define and more importantly, measure. We present a subjective user study of brightness and warmth, where a series of audio examples are processed with different audio effects. 26 participants rated the perceived level of brightness and warmth of various instrumental sequences for 5 different audio effects including bit depth reduction, compression and equalisation. Results show that 8 bit reduction tends to increase brightness and decrease warmth whereas 12 bit reduction tends to do the opposite, although this is very much dependent on the instrument. Interestingly, the most significant brightness changes, due to bit reduction, were obtained for bass sounds. For comparison purposes, instrument phrases were also processed with both an analogue compressor and an equalisation plugin to see if any subjective difference was noticed when simulating sonic characteristics that might be associated with warmth. Greater significance was observed when the sound excerpts were processed with the plugins being used to simulate the effects of bit depth reduction.