Diagnostic System Based on the Human Auditory–brain Model for Measuring Environmental Noise—an Application to Railway Noise

Sakai, Hiroyuki; Hotehama, Takuya; Ando, Yoichi; Prodi, Nicola; Pompoli, Roberto

doi:10.1006/jsvi.2001.3884

Cited by 18 publications

(7 citation statements)

References 9 publications

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“…Primary sensations}loudness, pitch, timbre and temporal duration-and spatial sensations}sub-jective diffuseness, image shift of sound source and apparent source width (ASW)}can be described by the temporal and spatial factors extracted from the autocorrelation function (ACF) and the interaural crosscorrelation function (IACF) respectively. It has been shown that environmental noise can be characterized by these factors [3][4][5]. Fundamental subjective attributes for the sound field in a concert hall are accurately described by the model of auditory-brain system when taking into account the contributions of the ACF and IACF mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Dissimilarity Judgments in Relation to Temporal and Spatial Factors for the Sound Field in an Existing Hall

Hotehama

Sato

Ando

2002

Journal of Sound and Vibration

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Section: Introductionmentioning

confidence: 99%

Dissimilarity Judgments in Relation to Temporal and Spatial Factors for the Sound Field in an Existing Hall

Hotehama

Sato

Ando

2002

Journal of Sound and Vibration

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“…Aircraft noise during landing and takeoff conditions was characterized with the physical factors calculated from ACF and IACF, while railway noise were also analyzed using ACF and IACF parameters. 14,15 In addition, IACF parameters have been utilized to describe the acoustical properties of floor impact sounds. ACF and IACF parameters of floor impact sounds generated by standard impact sources were analyzed, and it was found that the IACF of binaural signals differentiates the early perception of loudness and noisiness of heavy-weight impact sounds.…”

Section: Introductionmentioning

confidence: 99%

Subjective evaluation of heavy-weight floor impact sounds in relation to spatial characteristics

Jeon

Lee

Kim

et al. 2009

The Journal of the Acoustical Society of America

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This study investigated the effect of a spatial factor, the magnitude of interaural cross-correlation ͑IACC͒ function, on subjective responses to heavy-weight floor impact sounds. Heavy-weight impact sounds were generated by a heavy/soft impact source ͑impact ball͒ in real apartments, so that impact sound pressure levels ͑SPLs͒ ͑L Amax ͒ and IACC could be analyzed. Just noticeable differences ͑JNDs͒ of impact SPL and IACC were investigated through the use of impact ball sounds. JNDs were determined by the criteria of 75% correct answers by participants, and it was found that JNDs of impact SPL and IACC were around 1.5 dB and 0.12-0.13, respectively. In addition, the annoyance caused by an impact ball was evaluated by changes in these two parameters. The results show that annoyance increased with increasing impact SPL and with decreasing IACC; the contributions of the two parameters to the scale value of annoyance were 79.3% and 20.4%, respectively. This indicates that the effects of IACC should be considered for the evaluation of annoyance, and the subjective response to impact ball sounds can be improved by controlling IACC, as well as impact SPL.

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“…Similarly, in Fuji et al (2001) the evolution of the perceived attributes of aircraft sounds (during take offs and landings) were related to the values of the four ACF parameters, whereas in Sakai et al (2002), it was shown how the ACF parameters varied depending on the perceived characteristics of the trains being analyzed.…”

Section: A Broad Band Autocorrelation Function Features (Bb-acf)mentioning

confidence: 96%

“…A set of temporal factors computed from the ACF of broad band signals were employed to describe the primary auditory sensations (i.e., loudness, pitch, timbre, and duration sensation). In environmental acoustics, this technique was used to parameterize acoustic signatures of aircrafts (Fujii et al, 2001), road traffic (Fujii et al, 2002) and railway noises (Sakai et al, 2002), as well as diverse background noises (Yang and Barkana, 2009). In Fujii et al (2004) an automatic noise measurement system based on ACF signal features was proposed.…”

Section: B Previous Work On Acf Signal Analysismentioning

confidence: 99%

“…Ando proposed a set of four parameters derived from the autocorrelation function (ACF) that are related to acoustic phenomena perceived by humans (Ando, 2001). This theory was initially applied to the design of theaters and concert halls (Ando, 1998) and, subsequently, to the description of single environmental noise sources (Fujii et al, 2001;Sakai et al, 2002). However, to model the complex auditory scenes properly, the analysis might require greater detail than the seminal ACF analysis introduced by Ando (2001).…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Narrow-band autocorrelation function features for the automatic recognition of acoustic environments

Valero

Álías

2013

The Journal of the Acoustical Society of America

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Acoustic environments are typically composed of multiple sound sources of different typologies, making them especially complex to model and parameterize. To develop an automatic acoustic environment recognition system, this work proposes a spectro-temporal signal parameterization technique inspired by human perception. The proposed parameters are derived from the analysis of the autocorrelation function of narrow-band signals (NB-ACF) obtained from an auditory gammatone filter bank. Five features related to acoustic phenomena are extracted from the NB-ACF to parameterize sound signals. Experiments are conducted on a 4 h sound database (composed of 15 different acoustic environments) employing four different machine learning techniques: K-nearest neighbor, Gaussian mixture models, neural networks and support vector machines. The averaged recognition rates increase by 4.5% when using the proposed NB-ACF features instead of the popular Mel frequency cepstral coefficients. The improvement is even greater (5.6%) when the features are tested in acoustic environments from unknown locations. These results are attributed to the better modeling of the acoustic environments thanks to the complementarity of the spectro-temporal features derived from NB-ACF analysis.

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Diagnostic System Based on the Human Auditory–brain Model for Measuring Environmental Noise—an Application to Railway Noise

Cited by 18 publications

References 9 publications

Dissimilarity Judgments in Relation to Temporal and Spatial Factors for the Sound Field in an Existing Hall

Dissimilarity Judgments in Relation to Temporal and Spatial Factors for the Sound Field in an Existing Hall

Subjective evaluation of heavy-weight floor impact sounds in relation to spatial characteristics

Narrow-band autocorrelation function features for the automatic recognition of acoustic environments

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