A History of Audio Effects

Wilmering, Thomas; Moffat, David; Milo, Alessia; Sandler, Mark

doi:10.3390/app10030791

Cited by 21 publications

(18 citation statements)

References 63 publications

(78 reference statements)

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“…When people work together anywhere to produce anything "the coherence of situated action is tied in essential ways not to a priori prescriptions, but to the action's particular circumstances" [20, p. 21], or context. Technology plays a crucial role in a music production working context and the role of intelligent machines is growing [21]. And intelligent tools remain largely indifferent to the user's circumstances.…”

Section: A Case For Contextmentioning

confidence: 99%

Context-Aware Intelligent Mixing Systems

Lefford¹,

Bromham²,

Fazekas³

et al. 2021

J. Audio Eng. Soc.

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Intelligent Mixing Systems (IMS) are rapidly becoming integrated into music mixing and production workflows. The intelligences of a human mixer and IMS can be distinguished by their abilities to comprehend, assess, and appreciate context. Humans will factor context into decisions, particularly concerning the use and application of technologies. The utility of an IMS depends on both its affordances and the situation in which it is to be used. The appropriate use for conventional purposes, or its utility for misappropriation, is determined by the context. This study considers how context impacts mixing decisions and the use of technology, focusing on how the mixer's understanding of context can inform the use of IMS, and how the use of IMS can aid in informing a mixer of different contexts.

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Section: A Case For Contextmentioning

confidence: 99%

Context-Aware Intelligent Mixing Systems

Lefford¹,

Bromham²,

Fazekas³

et al. 2021

J. Audio Eng. Soc.

Self Cite

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“…• 4 files with modifications in the amplitude of the signal: 2 with amplifications of 20% and 40%, and 2 with attenuations of 20% and 40%. Specifically, dynamic compression [31] was used for this processing. It reduces the volume of loud sounds and amplifies (a percentage) the quiet ones.…”

Section: Data Augmentationmentioning

confidence: 99%

“…For the second network, the best training was achieved with six epochs and, again, a batch size equal to 32. For both networks, the initial learning rate [30] with 0.001 and the "adam" optimizer [31] were the parameters used in the training process.…”

Section: Design and Trainingmentioning

confidence: 99%

Cyber-Physical System for Environmental Monitoring Based on Deep Learning

Monedero

Barbancho

Márquez

et al. 2021

Sensors

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Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS. This system is based on convolutional neural networks (CNNs) and is focused on the different types of vocalization of two species of anurans. CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate tool for the classification of environmental sounds. The classification results obtained are excellent (97.53% overall accuracy) and can be considered a very promising use of the system for classifying other biological acoustic targets as well as analyzing biodiversity indices in the natural environment. The paper concludes by observing that the execution of this type of CNN, involving low-cost and reduced computing resources, are feasible for monitoring extensive natural areas. The use of CPS enables flexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes.

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“…A separate place is reserved here for A History of Audio Effects, a review article by Thomas Wilmering, David Moffat, Alessia Milo, and Mark B. Sandler [3], which discusses the development and evolution of audio effect technology along with highlighting major technical breakthroughs and the impact of available audio effects. This article describes also many analog techniques predating current digital methods.…”

Section: Review Articlementioning

confidence: 99%