The Finite Difference Time Domain (FDTD) method is becoming increasingly popular for room acoustics simulation. Yet, the literature on grid excitation methods is relatively sparse, and source functions are traditionally implemented in a hard or additive form using arbitrarily-shaped functions which do not necessarily obey the physical laws of sound generation. In this paper we formulate a source function based on a small pulsating sphere model. A physically plausible method to inject a source signal into the grid is derived from first principles, resulting in a source with a nearflat spectrum that does not scatter incoming waves. In the final discrete-time formulation, the source signal is the result of passing a Gaussian pulse through a digital filter simulating the dynamics of the pulsating sphere, hence facilitating a physically correct means to design source functions that generate a prescribed sound field.

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A dataset of audio clips was prepared and audio quality assessed by subjective testing. Encoded as digital signals, a large amount of feature-extraction was possible. A new objective metric is proposed, describing the Gaussian nature of a signal’s amplitude distribution. Correlations between objective measurements of the music signals and the subjective perception of their quality were found. Existing metrics were adjusted to match quality perception. A number of timbral, spatial, rhythmic and amplitude measures, in addition to predictions of emotional response, were found to be related to the perception of quality. The emotional features were found to have most importance, indicating a connection between quality and a unified set of subjective and objective parameters.

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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, η 2 = 0.008) as were the words used to describe the attributes on which quality was assessed (χ2 (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.

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