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The most exciting hypothesis in cognitive science right now is the theory that cognition is embodied. Like all good ideas in cognitive science, however, embodiment immediately came to mean six different things. The most common definitions involve the straight-forward claim that "states of the body modify states of the mind." However, the implications of embodiment are actually much more radical than this. If cognition can span the brain, body, and the environment, then the "states of mind" of disembodied cognitive science won't exist to be modified. Cognition will instead be an extended system assembled from a broad array of resources. Taking embodiment seriously therefore requires both new methods and theory. Here we outline four key steps that research programs should follow in order to fully engage with the implications of embodiment. The first step is to conduct a task analysis, which characterizes from a first person perspective the specific task that a perceiving-acting cognitive agent is faced with. The second step is to identify the task-relevant resources the agent has access to in order to solve the task. These resources can span brain, body, and environment. The third step is to identify how the agent can assemble these resources into a system capable of solving the problem at hand. The last step is to test the agent's performance to confirm that agent is actually using the solution identified in step 3. We explore these steps in more detail with reference to two useful examples (the outfielder problem and the A-not-B error), and introduce how to apply this analysis to the thorny question of language use. Embodied cognition is more than we think it is, and we have the tools we need to realize its full potential

Categorisation of distortion profiles in relation to audio quality

Since digital audio is encoded as discrete samples of the audio waveform, much can be said about a recording by the statistical properties of these samples. In this paper, a dataset of CD audio samples is analysed; the probability mass function of each audio clip informs a feature set which describes attributes of the musical recording related to loudness, dynamics and distortion. This allows musical recordings to be classified according to their “distortion character”, a concept which describes the nature of amplitude distortion in mastered audio. A subjective test was designed in which such recordings were rated according to the perception of their audio quality. It is shown that participants can discern between three different distortion characters; ratings of audio quality were significantly different (F(1; 2) = 5:72; p < 0:001; eta^2 = 0:008) as were the words used to describe the attributes on which quality was assessed (�Chi^2(8; N = 547) = 33:28; p < 0:001).This expands upon previous work showing links between the effects of dynamic range compression and audio quality in musical recordings, by highlighting perceptual differences

User-guided rendering of audio objects using an interactive genetic algorithm

Object-based audio allows for personalisation of content, perhaps to improve accessibility or to increase quality of experience more generally. This paper describes the design and evaluation of an interactive audio renderer, which is used to optimise an audio mix based on the feedback of the listener. A panel of 14 trained participants were recruited to trial the system. The range of audio mixes produced using the proposed system was comparable to the range of mixes achieved using a traditional fader-based mixing interface. Evaluation using the System Usability Scale showed a low level of physical and mental burden, making this a suitable interface for users with impairments, such as vision and/or mobility

Variation in multitrack mixes : analysis of low-level audio signal features

To further the development of intelligent music production tools, towards generating mixes that would realistically be created by a human mix-engineer, it is important to understand what kind of mixes can be created, and are typically created, by human mix-engineers. This paper presents an analysis of 1501 mixes, over 10 different songs, created by mix-engineers. The primary dimensions of variation in the full dataset of mixes were ‘amplitude’, ‘brightness’, ‘bass’ and ‘width’, as determined by feature-extraction and subsequent principal component analysis. The distribution of representative features approximated a normal distribution and this is then used to obtain general trends and tolerance bounds for these features. The results presented here are useful as parametric guidance for intelligent music production systems