Models of signal processing in human hearing

Sottek, Roland; Genuit, Klaus

doi:10.1016/j.aeue.2005.03.016

Cited by 32 publications

(13 citation statements)

References 5 publications

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“…Temporal structures and spectral patterns are more important factors in determining whether a sound makes an annoying or disturbing impression [43]. Therefore, it is suggested to normalise the level difference with respect to the idealised level difference.…”

Section: Loudness Level Differencementioning

confidence: 99%

Airborne sound insulation in terms of a loudness model

Neubauer

Kang

2014

Applied Acoustics

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A B S T R A C TOne of the main goals of building acoustics is the prediction of airborne sound insulation between rooms to determine the quality of sound protection. In many practical cases, however, the objective measures of the airborne sound insulation using procedures in standards are not in agreement with the subjective assessment. This paper, therefore, after reviewing the conventional model to calculate airborne sound insulation, introduces a calculation scheme based on loudness level linked with specific fluctuation strength, yielding a weighted normalised loudness level difference, L nor,w . By analysing the difference between standard airborne sound insulation values and the introduced weighted normalised loudness level difference, it is revealed that the sound pressure level that is transmitted through a partition decreases with increasing frequency, and this is independent of the type of signal and of the airborne sound insulation values (R' w -values), whereas if the transmitted signal is converted into a loudness level, it tends to rise with increasing frequency. Moreover, it is found that, whereas a simple level difference does not exhibit the effect of a given signal to the frequency-dependent airborne sound insulation curve, using L nor,w , a significant change can be observed, in terms of both computed and measured results. Furthermore, the frequency-dependent results allow more details to be investigated for a certain sound insulation. A comparison between the measured and predicted airborne sound insulation with no obvious malfunction suggests that at some frequency ranges, a hypothetical subjective related failure might occur. Overall, the proposed L nor,w could reveal detailed insights into the in situ measured airborne sound insulation compared with standard airborne sound insulation values. The frequency-dependent values discussed in this paper form a basis for developing a single-number index.

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Section: Loudness Level Differencementioning

confidence: 99%

Airborne sound insulation in terms of a loudness model

Neubauer

Kang

2014

Applied Acoustics

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“…In psychoacoustics, it has been found that the human hearings are generated by vibrations from mechanical transmission chain of the malleus, incus and the stapes in the middle ear, which stimulate different parts of the basilar membrane in the inner ear, thereby the auditory nerve [36]. The human auditory system consists of the ear (outer, middle and inner ears) and the auditory nerves.…”

Section: Human Auditory-perception Processmentioning

confidence: 99%

Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

Xing

Wang

Shi

et al. 2016

Mechanical Systems and Signal Processing

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“…The theory of using spectrograms for this application comes from the human hearing and brain system role as a spectral analyzer [1] [2]. The ear and brain search for dominant components in a spectrum.…”

Section: Spectrogramsmentioning

confidence: 99%

Intelligent Hearing Assistance Using Self-Organizing Feature Maps

Hodges

Zohdy

2014

TMLAI

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A novel hearing assistance system is proposed which classifies sounds and selectively tunes them according to the needs of the hearing impaired. This differs from the usual hearing aids available today in that it uses computational intelligence to filter and tune sounds based on real life categorical classifications. The system can significantly improve audibility for the hearing impaired, including bringing completely inaudible tones into audibility. For classification a self-organizing feature map is used with a vector of sound features from the joint time-frequency domain. The map is trained with input sounds until a map of neurons is clustered according to these. The resulting map is used to classify new sounds. Based on this classification, a sound can be tuned to improve audibility for the hearing impaired. Techniques proposed for audio output include gamma tone frequency filtering, Fourier compression, low pass filtering, spectral subtraction, and an original algorithm to choose how to best boost the amplitude of deaf frequencies.

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Models of signal processing in human hearing

Cited by 32 publications

References 5 publications

Airborne sound insulation in terms of a loudness model

Airborne sound insulation in terms of a loudness model

Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

Intelligent Hearing Assistance Using Self-Organizing Feature Maps

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