Recommending audio mixing workflows

This paper describes our work on Audio Advisor, a workflow recommender for audio mixing. We examine the process of eliciting, formalising and modelling the domain knowledge and expert’s experience. We are also describing the effects and problems associated with the knowledge formalisation processes. We decided to employ structured case-based reasoning using the myCBR 3 to capture the vagueness encountered in the audio domain. We detail on how we used extensive similarity measure modelling to counter the vagueness associated with the attempt to formalise knowledge about and descriptors of emotions. To improve usability we added GATE to process natural language queries within Audio Advisor. We demonstrate the use of the Audio Advisor software prototype and provide a first evaluation of the performance and quality of recommendations of Audio Advisor

Two-phased knowledge formalisation for hydrometallurgical gold ore process recommendation and validation

This paper describes an approach to externalising and formalising expert knowledge involved in the design and evaluation of hydrometallurgical process chains for gold ore treatment. The objective was to create a case-based reasoning application for recommending and validating a treatment process of gold ores. We describe a twofold approach. Formalising human expert knowledge about gold mining situations enables the retrieval of similar mining contexts and respective process chains, based on prospection data gathered from a potential gold mining site. Secondly, empirical knowledge on hydrometallurgical treatments is formalised. This enabled us to evaluate and, where needed, redesign the process chain that was recommended by the first aspect of our approach. The main problems with formalisation of knowledge in the domain of gold ore refinement are the diversity and the amount of parameters used in literature and by experts to describe a mining context. We demonstrate how similarity knowledge was used to formalise literature knowledge. The evaluation of data gathered from experiments with an initial prototype workflow recommender, Auric Adviser, provides promising results

A Comparative Study of Cognitive Systems for Learning

Learning is the modification of a behavioral tendency by experience. Memory and reasoning are the most important aspects for learning in humans; information is temporarily stored in the short-term memory and processed, compared with existing memories and stored in long-term memory, and can be re-used when needed. One way to describe an organized pattern of thought or behavior and the categories of information along with their relationships is by using schemas. A cognitive script is one form of a schema that evolves over multiple exposures to the same set of stimuli and/or repeated enactment of a particular behavior. This research aims to provide a comparative study between three cognitive systems/tools designed to allow learning, by using cognitive scripts representation. Since retrieving and re-using past experiences is the core of any learning process, the focus of this thesis is to examine the current existing cognitive systems and tools to evaluate their ability to retrieve past experiences. SOAR, myCBR and Pharaoh are three systems considered for this thesis. Linear and multi-branched cognitive scripts were considered in order to measure the capacity of those systems to allow learning using cognitive scripts representation. The results of this work show that SOAR, myCBR and Pharaoh took almost the same time to retrieve a set of similar cognitive scripts to a query script. However, SOAR was able to retrieve one similar script only, while myCBR and Pharaoh were able to retrieve multiple scripts. Pharaoh tops the other two system in its ability to handle multibranched scripts of different sizes and the way it considers context

The Design of Audio Mixing Software Displays to Support Critical Listening

PhDThe mixing desk metaphor found in Digital Audio Workstations (DAW) is built upon a specialised and technical knowledge of signal flow and audio engineering. However, since their inception the DAW has gained a far wider and less technically specialised user-base. Furthermore, the limited screen space of laptop and tablet computers, combined with potentially limitless tracks in current DAWs has resulted in the need for complex interface navigation during mixing which may inhibit a fluid and intuitive approach to mixing. The research outlined in this thesis explores novel designs for Graphical User Interfaces (GUIs) for mixing, which acknowledge the changing role of the user, the limited space of tablet and mobile computers screens and the limitations of human perception during cross modal activities (aural and visual). The author designs and conducts several experiments using non-expert participants drawn from several music technology courses, to assess and quantify the extent to which current DAW designs might influence mixing workflow, aiming our research especially at beginner and non-expert users. The results of our studies suggest that GUIs which load visual working memory, or force the user to mentally integrate visual information across the interface, can reduce the ability to hear subtle simultaneous changes to the audio. We use the analysis of these experiments to propose novel GUI designs that are better suited to human cross-modal perceptual limitations and which take into account the specific challenges and opportunities afforded by screen-based audio mixers. By so doing, we aim to support the user in achieving a more fluid and focused interaction while mixing, where the visual feedback supports and enhances the primary goal of attending to and modifying the audio content of the mix. In turn, it is hoped this will facilitate the artistic and creative approaches required by music computer users

Automating the Production of the Balance Mix in Music Production

Historically, the junior engineer is an individual who would assist the sound engineer to produce a mix by performing a number of mixing and pre-processing tasks ahead of the main session. With improvements in technology, these tasks can be done more efficiently, so many aspects of this role are now assigned to the lead engineer. Similarly, these technological advances mean amateur producers now have access to similar mixing tools at home, without the need for any studio time or record label investments. As the junior engineer’s role is now embedded into the process it creates a steeper learning curve for these amateur engineers, and adding time onto the mixing process. In order to build tools to help users overcome the hurdles associated with this increased workload, we first aim to quantify the role of a modern studio engineer. To do this, a production environment was built to collect session data, allowing subjects to construct a balance mix, which is the starting point of the mixing life-cycle. This balance-mix is generally designed to ensure that all the recordings in a mix are audible, as well as to build routing structures and apply pre-processing. Improvements in web technologies allow for this data-collection system to run in a browser, making remote data acquisition feasible in a short space of time. The data collected in this study was then used to develop a set of assistive tools, designed to be non-intrusive and to provide guidance, allowing the engineer to understand the process. From the data, grouping of the audio tracks proved to be one of the most important, yet overlooked tasks in the production life-cycle. This step is often misunderstood by novice engineers, and can enhance the quality of the final product. The first assistive tool we present in this thesis takes multi-track audio sessions and uses semantic information to group and label them. The system can work with any collection of audio tracks, and can be embedded into a poroduction environment. It was also apparent from the data that the minimisation of masking is a primary task of the mixing stage. We therefore present a tool which can automatically balance a mix by minimising the masking between separate audio tracks. Using evolutionary computing as a solver, the mix space can be searched effectively without the requirement for complex models to be trained on production data. The evaluation of these systems show they are capable of producing a session structure similar to that of a real engineer. This provides a balance mix which is routed and pre-processed, before creative mixing can take place. This provides an engineer with several steps completed for them, similar to the work of a junior engineer