Speech intelligibility plays a key role in determining the quality of verbal communication. It depends on the acoustic characteristics of the room and the signal-to-noise ratio (SNR). Background noise has three main components: HVAC noise, anthropic noise (student activity) and external activities noise, each of them with different noise spectra. In this work, measurements taken with a sound level meter during lectures in university classrooms are analyzed using advanced statistical techniques in order to detect and separate the various components which contribute to background noise. The same statistical techniques are used to characterize the speech level too, i.e. the signal in the SNR. From the raw data collected with a sound level meter, an asymmetrical distribution is built. Then four statistical techniques are applied: percentile levels, Gaussian mixture model based on peak detection, blind Gaussian mixture model and blind k-means clustering. Results are compared and discussed, highlighting the pros and cons of each technique.

D’Orazio D., D.S.D. (2019). Signal-to-noise ratio in university lecture halls with low intelligibility. Berlin : Deutsche Gesellschaft für Akustik (DEGA e.V.) [10.18154/RWTH-CONV-239418].

Signal-to-noise ratio in university lecture halls with low intelligibility

D’Orazio D.;DE SALVIO, DOMENICO
;
Anderlucci L.;Garai M.
2019

Abstract

Speech intelligibility plays a key role in determining the quality of verbal communication. It depends on the acoustic characteristics of the room and the signal-to-noise ratio (SNR). Background noise has three main components: HVAC noise, anthropic noise (student activity) and external activities noise, each of them with different noise spectra. In this work, measurements taken with a sound level meter during lectures in university classrooms are analyzed using advanced statistical techniques in order to detect and separate the various components which contribute to background noise. The same statistical techniques are used to characterize the speech level too, i.e. the signal in the SNR. From the raw data collected with a sound level meter, an asymmetrical distribution is built. Then four statistical techniques are applied: percentile levels, Gaussian mixture model based on peak detection, blind Gaussian mixture model and blind k-means clustering. Results are compared and discussed, highlighting the pros and cons of each technique.
2019
Proc. 23rd International Conference on Acoustics integrating 4th EAA Euroregio 2019 - ICA 2019
5917
5924
D’Orazio D., D.S.D. (2019). Signal-to-noise ratio in university lecture halls with low intelligibility. Berlin : Deutsche Gesellschaft für Akustik (DEGA e.V.) [10.18154/RWTH-CONV-239418].
D’Orazio D., De Salvio D., Anderlucci L., Garai M.
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/701367
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact