Spectral Modelling Synthesis of Vehicle Pass-by Noise

Yang Fu; Damian Thomas Murphy

Spectral Modelling Synthesis of Vehicle Pass-by Noise

Research output: Contribution to conference › Paper › peer-review

Abstract

Spectral Modelling Synthesis (SMS) is a sound synthesis technique that models time-varying spectra of given sounds as a collection of sinusoids plus a filtered noise component. Although originally utilized to produce musical sounds, this technique can also be extended for analysis, transformation and synthesis of a wide range of environmental sounds, such as traffic noise. Simplifications based on psychoacoustic analysis can be conducted during the modelling process to avoid redundant data, which leads to perceptual similarity between synthesized sounds and the original recordings of vehicle pass-by noise. In this paper, we investigate if this perceptual similarity can be described by objective metrics, and how to improve the synthesis by tuning the parameters in the SMS algorithm. The results showed that vehicle pass-by sounds characterized by tyre and engine noise can be well synthesized with different parameter sets in the SMS algorithm. Furthermore, it is found that Zwicker Roughness is a sensitive metric for measuring the perceptual similarity between original recordings and synthesized sounds as it varies significantly when tuning SMS parameters.

Original language	English
Number of pages	10
Publication status	Published - 27 Aug 2017
Event	InterNoise2017 - Hong Kong, China Duration: 27 Aug 2017 → 30 Jan 2018 http://www.internoise2017.org/

Conference

Conference	InterNoise2017
Country/Territory	China
City	Hong Kong
Period	27/08/17 → 30/01/18
Internet address	http://www.internoise2017.org/

Keywords

Auralization
Environmental Acoustics
soundscape
traffic modelling

Access to Document

Internoise2017_V2.0_Yang Fu_DTMFinal published version, 374 KBLicence: Unspecified

Cite this

@conference{125bbd6e29f6487cbddfcc1296449417,

title = "Spectral Modelling Synthesis of Vehicle Pass-by Noise",

abstract = "Spectral Modelling Synthesis (SMS) is a sound synthesis technique that models time-varying spectra of given sounds as a collection of sinusoids plus a filtered noise component. Although originally utilized to produce musical sounds, this technique can also be extended for analysis, transformation and synthesis of a wide range of environmental sounds, such as traffic noise. Simplifications based on psychoacoustic analysis can be conducted during the modelling process to avoid redundant data, which leads to perceptual similarity between synthesized sounds and the original recordings of vehicle pass-by noise. In this paper, we investigate if this perceptual similarity can be described by objective metrics, and how to improve the synthesis by tuning the parameters in the SMS algorithm. The results showed that vehicle pass-by sounds characterized by tyre and engine noise can be well synthesized with different parameter sets in the SMS algorithm. Furthermore, it is found that Zwicker Roughness is a sensitive metric for measuring the perceptual similarity between original recordings and synthesized sounds as it varies significantly when tuning SMS parameters.",

keywords = "Auralization, Environmental Acoustics, soundscape, traffic modelling",

author = "Yang Fu and Murphy, {Damian Thomas}",

year = "2017",

month = aug,

day = "27",

language = "English",

note = "InterNoise2017 ; Conference date: 27-08-2017 Through 30-01-2018",

url = "http://www.internoise2017.org/",

}

TY - CONF

T1 - Spectral Modelling Synthesis of Vehicle Pass-by Noise

AU - Fu, Yang

AU - Murphy, Damian Thomas

PY - 2017/8/27

Y1 - 2017/8/27

N2 - Spectral Modelling Synthesis (SMS) is a sound synthesis technique that models time-varying spectra of given sounds as a collection of sinusoids plus a filtered noise component. Although originally utilized to produce musical sounds, this technique can also be extended for analysis, transformation and synthesis of a wide range of environmental sounds, such as traffic noise. Simplifications based on psychoacoustic analysis can be conducted during the modelling process to avoid redundant data, which leads to perceptual similarity between synthesized sounds and the original recordings of vehicle pass-by noise. In this paper, we investigate if this perceptual similarity can be described by objective metrics, and how to improve the synthesis by tuning the parameters in the SMS algorithm. The results showed that vehicle pass-by sounds characterized by tyre and engine noise can be well synthesized with different parameter sets in the SMS algorithm. Furthermore, it is found that Zwicker Roughness is a sensitive metric for measuring the perceptual similarity between original recordings and synthesized sounds as it varies significantly when tuning SMS parameters.

AB - Spectral Modelling Synthesis (SMS) is a sound synthesis technique that models time-varying spectra of given sounds as a collection of sinusoids plus a filtered noise component. Although originally utilized to produce musical sounds, this technique can also be extended for analysis, transformation and synthesis of a wide range of environmental sounds, such as traffic noise. Simplifications based on psychoacoustic analysis can be conducted during the modelling process to avoid redundant data, which leads to perceptual similarity between synthesized sounds and the original recordings of vehicle pass-by noise. In this paper, we investigate if this perceptual similarity can be described by objective metrics, and how to improve the synthesis by tuning the parameters in the SMS algorithm. The results showed that vehicle pass-by sounds characterized by tyre and engine noise can be well synthesized with different parameter sets in the SMS algorithm. Furthermore, it is found that Zwicker Roughness is a sensitive metric for measuring the perceptual similarity between original recordings and synthesized sounds as it varies significantly when tuning SMS parameters.

KW - Auralization

KW - Environmental Acoustics

KW - soundscape

KW - traffic modelling

M3 - Paper

T2 - InterNoise2017

Y2 - 27 August 2017 through 30 January 2018

ER -