High Time for Temporal Variation: Improving Sonic Interaction with Auditory Interfaces

Foley, Liam; Schutz, Michael

doi:10.1109/mim.2021.9549230

Cited by 2 publications

(1 citation statement)

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“…Regarding the influence of the harmonic amplitude of the piano on the sound quality, the literature [14] first recorded the piano, and then took one of the signals, and performed simple processing on the harmonic amplitude, for example, the harmonic amplitude was formed proportionally, or the harmonic amplitude was decreased according to the law form a set of synth sounds, etc. Then compare these synthetic sounds with the original recording of the piano, and finally find that the sound quality after such simple processing is not as good as the original sound, at most only close to the original recording.…”

Section: Related Workmentioning

confidence: 99%

A Piano Single Tone Recognition and Classification Method Based on CNN Model

Geng,

He,

Zhou

2023

IJACSA

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In order to improve the recognition and classification effect of piano single tone, this paper combines the CNN (Convolutional Neural Networks) model to construct the piano single tone recognition and classification model, and equalizes the uniformly irradiated parabolic tone transmission hardware. In this paper, the analytic method is used to calculate the direction diagram of the tone transmission hardware, and the analytical expression for calculating the gain of the tone transmission hardware is obtained. Moreover, this paper gives the calculation and analytical expression of the hardware gain of the tone transmission in the main lobe, and obtains the calculation method of the relative position of the two tone transmission hardware by using the conversion relationship between the global coordinate and the local coordinate. Finally, the variation law of the received power with the azimuth/elevation angle of the receiving tone transmission hardware and the incident high-power microwave frequency is given. The experimental study shows that the piano single tone recognition and classification method based on CNN model proposed in this paper can play an important role in piano single tone recognition. This article improves the note recognition algorithm for piano music by combining note features with frequency spectrum to obtain note spectrum, which improves the accuracy of audio classification recognition.

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Section: Related Workmentioning

confidence: 99%

A Piano Single Tone Recognition and Classification Method Based on CNN Model

Geng,

He,

Zhou

2023

IJACSA

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More detectable, less annoying: Temporal variation in amplitude envelope and spectral content improves auditory interface efficacy

Foley

Schlesinger

Schutz

2022

The Journal of the Acoustical Society of America

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Auditory interfaces, such as auditory alarms, are useful tools for human computer interaction. Unfortunately, poor detectability and annoyance inhibit the efficacy of many interface sounds. Here, it is shown in two ways how moving beyond the traditional simplistic temporal structures of normative interface sounds can significantly improve auditory interface efficacy. First, participants rated tones with percussive amplitude envelopes as significantly less annoying than tones with flat amplitude envelopes. Crucially, this annoyance reduction did not come with a detection cost as percussive tones were detected more often than flat tones—particularly, at relatively low listening levels. Second, it was found that reductions in the duration of a tone's harmonics significantly lowered its annoyance without a commensurate reduction in detection. Together, these findings help inform our theoretical understanding of detection and annoyance of sound. In addition, they offer promising original design considerations for auditory interfaces.

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High Time for Temporal Variation: Improving Sonic Interaction with Auditory Interfaces

Cited by 2 publications

References 25 publications

A Piano Single Tone Recognition and Classification Method Based on CNN Model

A Piano Single Tone Recognition and Classification Method Based on CNN Model

More detectable, less annoying: Temporal variation in amplitude envelope and spectral content improves auditory interface efficacy

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