The autocorrelation-based analysis as a tool of sound perception in a reverberant field

A sound in a real space (e.g. a street or a room) is studied by acousticians as the relationship between an anechoic signal and the reverberant sound field. We define ‘anechoic signal’ a signal representing the pressure variation emitted by the sound source, while the ‘reverberant field’ is a field representing the sum of all the sound reflections of the environment in which the sound exists, delayed in time due to the positions of the sound source and the listener. The auditory ability of detecting a sound and assigning it to a source depends both on the anechoic signal and the reverberant sound field. This relationship has been analysed in acoustic literature using the autocorrelation properties of the anechoic signals and objective metrics of the sound field, the last ones being the ‘room criteria’ described in ISO 3382. In this context, the ‘effective duration’ of the autocorrelation function (τe) has been proposed as key factor to ‘preferred’ values of several room criteria in relation to different kind of music signals. Relying on some similarities between the definition of ‘auditory objects’ and the sound detection in a reverberant space, this paper proposes the use of τe as a potential tool to study and catalogue auditory objects.

[1]  B. Delgutte,et al.  Neural correlates of the pitch of complex tones. I. Pitch and pitch salience. , 1996, Journal of neurophysiology.

[2]  Tapio Lokki,et al.  Disentangling preference ratings of concert hall acoustics using subjective sensory profiles. , 2012, The Journal of the Acoustical Society of America.

[3]  Shuoxian Wu,et al.  Comparison of Different Calculation Methods of Effective Duration ( τ e ) of the Running Autocorrelation Function of Music Signals , 2011 .

[4]  Tapio Lokki,et al.  Anechoic recording system for symphony orchestra , 2008 .

[5]  Yoichi Ando,et al.  Subjective preference in relation to objective parameters of music sound fields with a single echo , 1977 .

[6]  R. Meddis,et al.  A unitary model of pitch perception. , 1997, The Journal of the Acoustical Society of America.

[7]  Manfred R. Schroeder,et al.  Comparative study of European concert halls: correlation of subjective preference with geometric and acoustic parameters , 1974 .

[8]  Yoichi Ando,et al.  Annoyance of bandpass-filtered noises in relation to the factor extracted from autocorrelation function. , 2004, The Journal of the Acoustical Society of America.

[9]  T. Griffiths,et al.  What is an auditory object? , 2004, Nature Reviews Neuroscience.

[10]  Dario D'Orazio,et al.  Extraction of the envelope from impulse responses using pre-processed energy detection for early decay estimation. , 2015, The Journal of the Acoustical Society of America.

[11]  K. Lau,et al.  On generalized harmonic analysis , 1980 .

[12]  L. Thurstone A law of comparative judgment. , 1994 .

[13]  A. de Cheveigné Cancellation model of pitch perception. , 1998, The Journal of the Acoustical Society of America.

[14]  Mikio Tohyama,et al.  ESTIMATION OF SPEECH COMPONENTS BY ACF ANALYSIS IN A NOISY ENVIRONMENT , 2001 .

[15]  Y. Cohen,et al.  The what, where and how of auditory-object perception , 2013, Nature Reviews Neuroscience.

[16]  Dario D'Orazio,et al.  Acoustic measurements in eleven Italian opera houses: Correlations between room criteria and considerations on the local evolution of a typology , 2015 .

[17]  J. Licklider,et al.  A duplex theory of pitch perception , 1951, Experientia.

[18]  Massimo Garai,et al.  A comparison of methods to compute the "effective duration" of the autocorrelation function and an alternative proposal. , 2011, The Journal of the Acoustical Society of America.

[19]  W A Yost,et al.  A time domain description for the pitch strength of iterated rippled noise. , 1996, The Journal of the Acoustical Society of America.

[20]  Yoichi Ando,et al.  MAGNETOENCEPHALOGRAPHIC RESPONSES CORRESPONDING TO INDIVIDUAL SUBJECTIVE PREFERENCE OF SOUND FIELDS , 2002 .