Soumya Sai Vanka

Effective music mixing requires technical and creative finesse, but clear communication with the client is crucial. The mixing engineer must grasp the client’s expectations and preferences and collaborate to achieve the desired sound. The tacit agreement for the desired sound of the mix is established using guides like reference songs and demo mixes exchanged between the artist and the engineer. This paper presents the findings of a two-phased exploratory study aimed at understanding howprofessionalmixing engineers interact with clients and use their feedback to guide the mixing process. For phase one, semistructured interviews were conducted with five mixing engineers with the aim of gathering insights about their communication strategies, creative processes, and decision-making criteria. Based on the inferences from these interviews, an online questionnairewas designed and administered to a larger group of 22 mixing engineers during the second phase. The results shed light on the importance of collaboration and intention in the mixing process and can inform the development of smart multitrack mixing systems. By highlighting the significance of these findings, this paper contributes to the research on the collaborative nature of music production and provides actionable recommendations for the design and implementation of innovative mixing tools.

The integration of artificial intelligence (AI) technology in the music industry is driving a significant change in the way music is being composed, produced and mixed. This study investigates the current state of AI in the mixing workflows and its adoption by different user groups. Through semi-structured interviews, a questionnaire-based study, and analyzing web forums, the study confirms three user groups comprising amateurs, pro-ams, and professionals. Our findings show that while AI mixing tools can simplify the process and provide decent results for amateurs, pro-ams seek precise control and customization options, while professionals desire control and customization options in addition to assistive and collaborative technologies. The study provides strategies for designing effective AI mixing tools for different user groups and outlines future directions.

Mixing is a central task within audio post-production where expert knowledge is required to deliver professional quality content, encompassing both technical and creative considerations. Recently, deep learning approaches have been introduced that aim to address this challenge by generating a cohesive mixture of a set of recordings as would an audio engineer. These approaches leverage large-scale datasets and therefore have the potential to outperform traditional approaches based on expert systems, but bring their own unique set of challenges. In this tutorial, we begin by providing an introduction to the mixing process from the perspective of an audio engineer, along with a discussion of the tools used in the process from a signal processing perspective. We then discuss a series of recent deep learning approaches and relevant datasets, providing code to build, train, and evaluate these systems. Future directions and challenges will be discussed, including new deep learning systems, evaluation methods, and approaches to address dataset availability. Our goal is to provide a starting point for researchers working in MIR who have little to no experience in audio engineering so they can easily begin addressing problems in this domain. In addition, our tutorial may be of interest to researchers outside of MIR, but with a background in audio engineering or signal processing, who are interested in gaining exposure to current approaches in deep learning.