In this paper several different digital soft clipping algorithms are described and analysed. It is discussed how the quality of each algorithm can be estimated. A testing methodology is devised to show the levels of nonlinearities produced as a function of the input signal amplitude. It is proposed that, while all soft clipping algorithms produce higher order nonlinearities, the instability of the produced harmonics plays a crucial role in the transparency of the effect. Existing and novel clipping algorithms are thus compared and classified based on their measured properties, including total harmonic distortion and inter-modulation distortion estimates. This paper proposes a conclusion related to the quality and properties of different algorithms.

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The measurement of perceived loudness is a difficult yet important task with a multitude of applications such as loudness alignment of complex stimuli and loudness restoration for the hearing impaired. Although computational hearing models exist, few are able to accurately predict the binaural loudness of everyday sounds. Such models demand excessive processing power making real-time loudness metering problematic. In this work, the dynamic auditory loudness models of Glasberg and Moore (J. Audio Eng. Soc., 2002) and Chen and Hu (IEEE ICASSP, 2012) are presented, extended and realised as binaural loudness meters. The performance bottlenecks are identified and alleviated by reducing the complexity of the excitation transformation stages. The effects of three parameters (hop size, spectral compression and filter spacing) on model predictions are analysed and discussed within the context of features used by scientists and engineers to quantify and monitor the perceived loudness of music and speech. Parameter values are presented and perceptual implications are described.

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In sound production, engineers cascade processing modules at various points in a mix to apply audio effects to channels and busses. Previous studies have investigated the automation of parameter settings based on external semantic cues. In this study, we provide an analysis of the ways in which participants apply full processing chains to musical audio. We identify trends in audio effect usage as a function of instrument type and descriptive terms, and show that processing chain usage acts as an effective way of organising timbral adjectives in low-dimensional space. Finally, we present a model for full processing chain recommendation using a Markov Chain and show that the system’s outputs are highly correlated with a dataset of user-generated processing chains.

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In musical timbre, two of the most commonly used perceptual dimensions are warmth and brightness. In this study, we develop a model capable of accurately controlling the warmth and brightness of an audio source using a single parameter. To do this, we first identify the most salient audio features associated with the chosen descriptors by applying dimensionality reduction to a dataset of annotated timbral transformations. Here, strong positive correlations are found between the centroid of various spectral representations and the most salient principal components. From this, we build a system designed to manipulate the audio features directly using a combination of linear and nonlinear processing modules. To validate the model, we conduct a series of subjective listening tests, and show that up to 80% of participants are able to allocate the correct term, or synonyms thereof, to a set of processed audio samples. Objectively, we show low Mahalanobis distances between the processed samples and clusters of the same timbral adjective in the low-dimensional timbre space.

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