Rating Airborne Sound Insulation in Terms of the Annoyance. and Loudness of Transmitted Speech and Music Sounds

Transcription

1 Rating Airborne Sound Insulation in Terms of the and Loudness of Transmitted and Sounds DBR-RR- Park, H.K.; Bradley, J.S.; Gover, B.N. November 008

2 Rating Airborne Sound Insulation in Terms of the and Loudness of Transmitted and Sounds Hyeon Ku Park, John S. Bradley and Bradford N. Gover IRC Research Report, IRC RR- November, 008

3 ABSTRACT This report describes the results of evaluations of airborne sound insulation measures in terms of the annoyance and ness of transmitted speech and music sounds. Subjects rated sounds simulating transmission through 0 different walls with a wide range of sound transmission characteristics. The evaluated measures included the standard Sound Transmission Class (STC) and the Weighted Sound Reduction Index (R w ) as well as variations of these measures. In addition, many other signal-to-noise type measures and ness-related measures were included in the evaluations. The results showed that the frequencies important for rating speech sounds were quite different to those required for music sounds. As a consequence, measures that predicted ratings of transmitted music sounds well tended to be less successful for speech sounds and vice versa. Some compromise measures were found and new spectrum adaptation terms added to R w values were seen to be a successful and practical means of more accurately rating airborne sound insulation. RR- -

4 Table of Contents Page Abstract Contents Acknowledgements. Introduction. Experimental Details. Test facility., music and noise signals. Simulated Transmission Loss characteristics. Procedure for annoyance and ness ratings 9. Subjects 0. Data analysis procedures. Evaluation of Standard Ratings R w and STC. versus STC for music and speech. versus R w for music and speech. Loudness versus STC and R w for music and speech. Comparison of annoyance and ness ratings. Effects of Included Frequencies for Arithmetic Average TL Measures. More important frequencies for TL data. Included frequencies for Arithmetic Average of TL values 8. Comparisons of some better Arithmetic Average TL measures. Effects of included frequencies for Energy Average TL measures. Variations of STC Ratings 9. Variations of the 8 db Rule 9. Variations of the Total Allowed Deviation. Variations of R w Measures. Evaluation of standard Spectrum Adaptation Terms. Evaluation of variations of Spectrum Adaptation Terms. Variations of included frequencies. Evaluation of Intelligibility Related Measures, Loudness Ratings and related Measures 8. Audibility of Transmitted Sounds 9. A-weighted Level Differences for Rating Sound Insulation 0. Discussion, Recommendations and Conclusions 9 Appendix I. Ratings Including Presentation of a Reference Sound. Appendix II. Effects of Language on ratings of Transmitted Sounds 9 References 8 RR- -

5 Acknowledgements The authors would like to acknowledge that a Korea Research Foundation Grant, funded by the Korean Government (MOEHRD) (KRF-00--D0000) to Dr. Hyeon Ku Park, supported his contribution to this work. RR- -

6 . Introduction Airborne sound transmission through partitions separating dwellings and other spaces is measured over a range of frequencies in standardized tests. In North America the ASTM E90 [] procedure is used in the laboratory and the ASTM E [] procedure is used in field situations. In most other countries the ISO 0 procedures [] are usually followed to measure airborne sound transmission through walls and floors. These two approaches are very similar and include single number ratings to reduce the results at a number of frequencies to a single numerical value. The STC (Sound Transmission Class) from the ASTM E standard [] and the R w (Weighted Sound Reduction Index) from the ISO - standard [] are quite similar in their derivation and are widely used to specify the required sound insulation in various situations such as between homes. The results of previous research [,] showed that the ISO Weighted Sound Reduction Index (R w ) and ASTM Sound Transmission Class (STC) ratings were not good predictors of the intelligibility of transmitted speech sounds. Although the total allowed deficiency of db in the STC and R w measures was found to be acceptable, the maximum allowed deficiency of the 8 db rule was not helpful for predicting the intelligibility of transmitted speech. However, measures that are related to the intelligibility of speech, such as the Articulation Index (AI) [8], the Intelligibility Index (SII) [9], and the Articulation Class (AC) [0], were more strongly related with the mean intelligibility scores. When other types of possible sound insulation ratings were considered, those that were based on arithmetic averaging of decibel values over frequency were more successful predictors of responses than those based on energy averages over various frequency bands. This was expected, because the well-established AI measure is based on the same concept of a frequency-weighted summation of the signal-to-noise ratios in decibels over frequencies important for the intelligibility of speech. Measures that limit the included frequency bands or weight their importance according to their influence on the intelligibility of speech were also seen to be better predictors of the intelligibility of the transmitted speech. Two more successful approaches included an arithmetic average of transmission loss (TL) values over speech frequencies and a new speech spectrum adaptation term for the ISO R w procedure. It is well known that many types of sounds such as music, speech, television/radio, vacuum cleaners etc. can be disturbing to neighbours []. Because the initial study only rated sound insulation in terms of the intelligibility of speech, further studies were needed to consider other ratings of airborne sound insulation and other types of sounds. The present study responded to this need and compared sound insulation ratings in terms of how well they predicted subjective ratings of the annoyance and ness of transmitted music and speech sounds. As in the previous work, standard sound insulation ratings were evaluated as well as various other possible measures including those found successful in the first study []. RR- -

7 . Experimental Details. Test facility All tests were conducted in the Room Acoustics Test Space in Building M-9 at the National Research Council in Ottawa. This is a room measuring 9. m long by. m wide by. m high. It is constructed from concrete and is resting on springs to make it well sound-isolated from unwanted sounds. For the present study, the interior walls of the room were lined with 0 cmthick absorbing foam, which was covered by curtains. There was a conventional T-bar ceiling with mm-thick glass fibre ceiling tiles installed, and the floor was covered with thin carpet. This interior treatment yielded a quite dead space, enabling the experimenters to completely control the sounds within the room. The background noise level in the room was about dba (measured with the sound simulation system turned off). The test speech and music sounds were played over speakers positioned at the front of the room located m in front of the subject. The background noise was played over another set of speakers positioned above the ceiling, directly above the subject. Figure shows a diagram of the setup. Ambient noise speakers Foam Transmitted speech and music speakers Listener Ceiling Curtain Figure. Schematic of cross-section through Room Acoustics Test Space showing the location of the listener and the speakers used to generate the test sound fields. A block diagram of the electro-acoustic system used to produce the test sounds is shown in Figure. The two blocks labelled DME are Yamaha Digital Mixing Engines, which are highly flexible signal processing boxes, able to perform the functions of many interconnected devices such as equalizers, filters, oscillators, etc. The outputs of the DMEs run via the power amplifiers into high-quality speaker systems (Paradigm Compact Monitors and Paradigm PW sub-woofers). One component in each DME was initially configured to equalize the playback path through the power amplifiers and speakers to be flat at the position of the listener s head (± db from 0 to 000 Hz). The background noises for the test sound fields were generated by a component of one of the DME units that can generate broadband noise. This same unit shaped the spectrum and adjusted the level as desired. One channel of the noise output was delayed by 00 ms relative to the other to avoid the two noise signals arriving coherently at the listener s position. This avoided any unnatural perceptual effects when the listener moved their head. The speech and music sounds were generated from playback of recorded source material stored on the computer in -bit,. khz wav-file format. The output of the sound card ran into the second DME, which performed the necessary equalization and level adjustment. The required RR- -

8 equalizations to simulate the transmission loss of each of the 0 walls were stored in separate scenes, which can be selected from the computer over the MIDI interface. Ambient Noise sub-woofer Transmitted sub-woofer power amplifiers DME MIDI DME RS MIDI RS optical digital audio link Figure. Block diagram of the computer controlled electro-acoustic system used to create the test sounds.., music and noise signals The speech test materials were the Havard sentences []. Three different sentences were played through each simulated wall. Three music samples were used which were selected from a large number of pieces as being potentially annoying and different styles of music. They were: Rap music (The Roots, I Remain Calm ), House music (Dizzee Rascal, Stand Up Tall ) and Pop music, (Cyndi Lauper, She Bop ). The average spectra of the speech and music sounds, before modification to simulate transmission through various walls, are shown in Figure. The music had relatively more energy at lower frequencies, while the energy of speech was strongest in midfrequencies (00 Hz to khz). In the first test in which the subjects rated the annoyance of the sounds, the average SPL of the sound sources were 8.0 db (. dba) for the music and 80.9 db (. dba) for the speech. The source levels were fixed at levels such that the transmitted sounds varied from barely audible to quite for the range of simulated walls Noise 0 SPL, db k k k 8k Frequency, Hz Figure. Average spectra of speech and noise source signals before transmission through the walls as well as the spectrum of the simulated ambient noise. RR- -

9 The source sound levels for the second test, in which subjects rated the ness of the sounds, were reduced by db relative to those for the annoyance test to make it possible to also determine the threshold of audibility of the transmitted sounds. Figure also includes the spectrum of the simulated ambient noise at the listener s position, which was held constant at an overall level of. dba.. Simulated Transmission Loss characteristics The 0 simulated wall transmission loss characteristics were the same as in the previous speech intelligibility experiment []. They simulated real walls with a wide range of STC ratings evenly distributed between STC and STC 8 as obtained in standard laboratory sound transmission loss tests. When the speech sounds, at a common fixed source level, were played through these simulated walls, intelligibility scores varied from about 0% to 00%. This range of simulated walls was assumed to be equally appropriate for subjective evaluations of the annoyance of the transmitted speech and music sounds. The sound transmission loss versus frequency characteristics for the 0 selected walls are shown in Figure. The shapes and overall levels of the transmission loss values vary considerably and the data represent a broad range of real walls as tested in laboratory conditions. The walls containing wood studs, steel studs and concrete blocks are separately identified in this figure and are seen to have quite different characteristics TL, db Wood studs Steel studs Concrete block Frequency, Hz Figure. Sound Transmission loss versus /-octave band frequency for the 0 walls simulated in the listening tests, where those containing wood studs, steel studs and concrete blocks are separately identified. The spectra of the transmitted music and speech sounds combined with the noise are plotted in Figures and for the average music and speech sounds respectively. The transmitted music plus noise spectra in Figure show the greatest variations at low frequencies. Although the speech sounds (see Figure ) also show large low frequency variations above the level of the ambient noise, they also show significant variations in the 0 to 00 Hz range. RR- -

10 0 0 Wood studs Steel studs Concrete block Ambient noise SPL of music, db k k k Frequency, Hz Figure. Spectra of combined transmitted music sounds and ambient noise in the annoyancerating test for the average of the music samples combined with the ambient noise, for each of the 0 walls. 0 0 Wood studs Steel studs Concrete block Ambient noise SPL of speech, db k k k Frequency, Hz Figure. Spectra of combined average transmitted speech sounds and ambient noise in the annoyance-rating test for the average of the speech samples combined with the ambient noise, for each of the 0 walls. Table provides a summary of the wall constructions and their STC and R w ratings. The walls are common constructions in North America with STC ratings varying from a quite modest (STC ) to a very good sound insulation rating (STC 8). RR- - 8

11 No. Descriptor STC rating R w rating G_GFB90_WS89_G G_SS_G G_SS_G G_SS90_G G_SS90_G G_GFB90_SS90_G 9 G_SS0_AIR0_SS0_G G_GFB90_SS90_G 0 9 G_GFB_SS_G 0 BLK90 G_MFB0_SS90_G BLK0 G_GFB90_SS90_G BLK90_PAI 8 8 G_BLK90_G 9 0 BLK G_GFB90_SS90_G 0 8 G_GFB90_SS90_G 9 PAI_BLK0_WFUR0_GFB8_G 0 PAI_BLK0_GFB8_WFUR0_G 8 Table. Summary of simulated wall constructions and their STC and R w ratings. The descriptor codes are explained in Table. For example, wall number, which is described as, G_GFB90_SS90_G, indicates the various layers of the construction from one side to the other. In this case the construction includes: mm gypsum board (G), 90 mm glass fibre batts in the stud cavity (GFB90), 90 mm steel studs (SS90), and then layers of mm gypsum board (G). Descriptor Explanation Descriptor Explanation AIR Air space PAI Paint BLK Concrete block SS Steel stud G Gypsum board WFUR Wood furring GFB Glass fibre batt WS Wood stud MFB Mineral fibre batt Table. Explanation of symbols used to describe the simulated wall constructions.. Procedure for annoyance and ness ratings To familiarize subjects with the types of sounds they would hear, they first listened to a practice test consisting of sounds made up of different test sentences and music samples. Subjects heard each type of sound (i.e. speech or music) played through one of different simulated walls that varied from very low to very high STC rating. They were told that the practice examples were representative of the full range of conditions that they would hear in the full test. The practice test followed an initial hearing sensitivity test. In the full test, listeners heard different Harvard sentences and different music samples through each of the 0 simulated walls for a total of 0 sentences and 0 music samples. The order of the speech and music samples and of the walls was randomized so that subjects heard RR- - 9

12 conditions in one of three different randomized orders. In the experimental procedure, only the simulated transmission characteristics of the walls were varied. The effective speech or music source level and the ambient noise level at the listener s position remained constant throughout the tests. The results were analyzed in terms of the average annoyance or ness scores for all listeners and all test sentences or for all music samples for each wall. That is, each average annoyance or ness rating of transmitted speech sounds was an average of the scores for sentences and all subjects for each wall. Similarly, each average annoyance or ness rating of transmitted music sounds was the average of the ratings of music samples and all subjects for each test wall. In the annoyance tests, subjects were asked to imagine they were at home trying to relax. In this context they were asked to rate how annoying they would find each of the transmitted test sounds. They rated annoyance on a -point scale with the end points labelled Not at all annoying and Extremely annoying. The mid-point was labelled Moderately annoying. An extra annoyance test was carried out in which subjects heard the same music or speech samples first played through an average wall and then through one of the test walls. These results are included in Appendix I. It was thought that this procedure might lead to more reliable results because subjects could always compare with the reference average wall case. In the second part of the main study subjects rated the ness of the same transmitted speech and music samples. In this test subjects rated the ness of the sounds on an 8-point scale. Point number was labelled Not at all, point number Extremely and point number Moderately. In the ness rating test, they could also give a 0 response which was labelled Not audible making it an 8-point scale. To ensure that there were a number of inaudible cases, the source levels for the ness experiment were reduced by db relative to those of the annoyance experiment. This made it possible to determine the threshold of audibility as the point at which 0% of the subjects could just hear speech or music sounds. In both the annoyance and the ness experiments, subjects heard the test speech or music sounds followed by a s gap as illustrated in Figure. In the s gap they rated the annoyance or ness using a numeric keypad and their results were written directly to a computer file. Test sound Test sound Test sound Figure. Sequence of presentation of test sounds with second intervals for the subjects to rate the annoyance or ness of the test sounds.. Subjects s Ten subjects completed the annoyance test. They were all NRC employees who volunteered to do the test after being approached by an request for volunteers. The research was carried out according to the procedure approved by the NRC Research Ethics Board (Protocol 00-). Twenty subjects were tested for the ness test. These subjects were hired from a temporary employment agency and were shared with other IRC projects (See Protocol 00-). The additional annoyance test, that included reference sounds to aid judgments, was carried out by 8 subjects, who were also hired from the temporary employment agency under the same protocol. All subjects were first given a hearing sensitivity test. Their pure tone average (PTA) hearing levels (HL) varied from -8.0 db to 9. db (with an average of. db) for the annoyance test and from - db to db (with an averaged. db) for the ness test. (PTA values are averages of s RR- - 0

13 HL values over the test frequencies 00, k and k Hz). These mean PTA values are a little better than the 0 th percentile levels for normal hearing listeners [].. Data analysis procedures Most of the following analyses consist of plots of the mean subjective ratings versus a sound insulation rating measure such as STC. To test the strength of the correlation between the subjective ratings and the sound insulation measures, Boltzmann equations were fitted to the plots and the related R values calculated. The Boltzmann equation is given by the following, A A y 0 + e where, = + ( x x )/ dx A A is the y-value for x = - ( for annoyance test) A is the y-value for x = + ( for annoyance test) x 0 is the x-value of mean y-value, that is the x-value when y = for the annoyance test dx is related to the slope of the mid-part of the regression line The Boltzmann equation tends to fit the expected subjective ratings well because the fitted equations gradually approach some minimum and maximum values for the extreme values of the x-value, that is, the sound insulation measure. (e.g. for annoyance responses, the A and A values could be set to either or, the lowest and highest response scale values respectively). Almost all of the results presented in this report were statistically significant. Since there were always 0 data points and the same format of regression equation, the significance is simply related to the R value (i.e. the coefficient of determination). Any R value 0.9 is statistically significant at p<0.0 and an R value 0. at p<0.0. () RR- -

14 . Evaluation of Standard Ratings R w and STC. versus STC for music and speech Figure 8 shows a plot of mean annoyance ratings versus the STC values of the 0 walls. The related R values from the Boltzmann equation fits to these data are included in the figure title. STC values are better related to the annoyance ratings of transmitted speech sounds than to the annoyance ratings of transmitted music sounds. That is, the R values indicate that STC is a better predictor of annoyance responses to speech sounds than to those for music sounds. However, one cannot say that the magnitude of annoyance ratings of music sounds is generally greater than for speech sounds because this would depend on source levels of both types of sounds. In this study, the source levels for each type of sound were adjusted to give a more complete range of responses. The results for annoyance ratings of music sounds for walls W, W, and W8 deviate more than other points from the regression line. As Figure 9 shows, the higher transmitted sounds at low frequencies for these three walls are the components that most exceed the ambient noise levels and hence would be most obvious to listeners. The reduced low frequency transmission loss of these walls, leads to these higher transmitted low frequency sound levels and probably causes the increased scatter shown in Figure 8 and the weaker relationship between annoyance ratings of music sounds and STC values. W W W STC Figure 8. Mean annoyance ratings versus STC for music and speech sounds. [, R =0.8,, R =0.8]. IRC RR- -

15 SPL, db W W W W W W8 Ambient noise k k k Frequency, Hz Figure. 9. Spectra of mean transmitted music sound levels through several walls showing the increased low frequency levels for walls W, W and W8.. versus R w for music and speech The relationships between annoyance responses to music and speech sounds and R w values are very similar to those with STC values shown in Figure 8. The related R values shown in the title of Figure 0 have slightly higher values than those for STC values. Again annoyance ratings of music sounds are less well predicted and data for the same three walls (W, W and W8) seem to deviate more from the main trend of the annoyance ratings of music sounds due to the increased low frequency transmitted sounds for these three walls. W W W R w Figure 0. Mean annoyance ratings versus R w values for music and speech sounds, [, R =0.98 (0.8),, R =0.890 (0.8)], (values in brackets are R for annoyance versus STC values from Figure 8). IRC RR- -

16 . Loudness versus STC and R w for music and speech Figure compares mean ratings of the ness of the transmitted speech and music sounds plotted versus STC values. Figure shows similar results plotted versus the R w ratings of the test walls. The results are similar in form to the previous plots of annoyance ratings in that STC and R w are better predictors of the responses to speech sounds than to music sounds. Loudness W W W8 0 not audible STC Figure. Mean ness ratings versus STC values for speech and music sounds, [, R =0. (0.8),, R = 0.88 (0.8)], (values in brackets are R values for annoyance versus STC from Figure 8). Loudness 0 not audible R w Figure. Mean ness ratings for music and speech sounds versus R w values, [, R =0.9,, R =0.90] Table summarizes the results of the Boltzmann equations fitted to the annoyance and ness ratings in terms of either STC or R w values. R values are higher for regression lines fitted to responses to speech sounds than those for music sounds and R w values are a little better than STC values as predictors of annoyance and ness ratings of both speech and music sounds. Loudness and annoyance ratings lead to very similar relationships and neither ness nor annoyance responses is distinctly better related to these two sound insulation measures. IRC RR- -

17 Loudness Symbol Type f f R X 0 dx R X 0 dx STC music k speech k R w music 00.k speech 00.k Table. Summary of R values and regression coefficients of Boltzmann equations fitted to annoyance and ness ratings versus the standard ratings (STC and R w ).. Comparison of annoyance and ness ratings Figure compares annoyance and ness ratings of music sounds plotted versus STC values. Although the mean annoyance ratings are higher than the mean ness ratings, the forms of the regression lines are quite similar and the related R values are also very similar. As previously described, the response scales were a little different in that the ness responses included a 0 value to indicate inaudible speech or music sounds. These differences and the different source levels may explain the difference in average values of the ness and annoyance responses. or Loudness 0 Loudness of music to music STC Figure.. Comparison of mean annoyance and mean ness ratings plotted versus STC values for music sounds, [, R =0.8, Loudness, R =0.]. Figure similarly compares annoyance and ness ratings of speech sounds and shows very similar relationships between annoyance ratings and ness ratings versus STC values for speech sounds. For both speech and music sounds the Boltzmann fits for annoyance and ness responses have similar slopes at the mid-points of the curves as indicated by the similar dx values (see Table ). The differences in overall average ness and annoyance responses are to be expected due to the differences in response scales and source sound levels used. It is important to note that other than these obvious differences, annoyance and ness responses are very similarly related to STC and R w values. IRC RR- -

18 Loudness of speech to speech or Loudness Figure. Comparison of mean annoyance and mean ness ratings plotted versus STC values for speech sounds, [, R =0.8, Loudness, R =0.88]. Figure plots annoyance responses versus ness responses. The near linear relationships and high R values again indicate that these different concepts lead to quite similar results. That is, sounds that are judged to be er will usually be judged to be more annoying in these experiments. It is probably not necessary to assess both ness and annoyance responses as they seem to provide essentially the same information. STC 0 Loudness Figure. Relationship between mean annoyance and mean ness ratings for both speech and music sounds, [, R =0.9,, R =0.9] IRC RR- -

19 . Effects of Included Frequencies for Arithmetic Average TL Measures. More important frequencies for TL data Correlation analyses were carried out between subjective responses from the annoyance and ness tests and sound transmission loss (TL) values at each /-octave band frequency for the 0 walls. Figures shows the correlation coefficients between the two responses and TL values for the music sounds as a function of frequency. Similar results for the speech sounds are given in Figure. Although the results for annoyance and ness response are very similar in each graph, there are large differences between the two graphs. That is, the frequencies most important for responses to music sounds are quite different than those most important for speech sounds. The dash-dotted line with cross symbols is the standard deviations of the wall TL values, Plotted according to the scale of the right hand axis so that they are more easily compared with the correlation coefficient values. Correlation Coefficient Loudness STDev of wall TL Standard Deviation, db k k k Frequency, Hz Figure. Correlation coefficients between mean annoyance and ness ratings of music sounds and /-octave band TL values. The standard deviations (STDev) of the wall TL values are also plotted for comparison in terms of the right hand axis scale. For the responses to music sounds in Figure, there is a strong similarity between the variations of the correlation coefficients and the variations of the standard deviations of TL values with frequency. This indicates that the correlations are stronger when there is more variation in the TL values. Figure showed that the transmitted music spectra have higher levels and larger variations in levels at low frequencies and to a lesser extent at high frequencies. Thus, for music, the low and high frequency TL values best predict the annoyance and ness responses to the music sounds. Compared to Figure, Figure shows stronger correlation coefficients at mid-frequencies for the responses to speech sounds. Figure, showed that the spectra of the transmitted speech sounds had more prominent mid-frequency components than did the music sounds. Of course, it is well known that mid-and high frequency sounds contribute most to the intelligibility of speech and that lower frequency components, even when present are much less important for speech. Presumably, it is the same frequencies of transmitted speech sounds that contribute most to the annoyance and ness responses to the speech sounds. IRC RR- -

20 Correlation Coefficient Loudness STDev of wall TL Standard Deviation, db k k k Frequency, Hz Figure. Correlation coefficients versus frequency between mean annoyance and mean ness ratings of speech sounds and /-octave band TL values. The standard deviations (STDev) of the wall TL values are also plotted for comparison in terms of the right hand axis scale.. Included frequencies for Arithmetic Averages TL values In previous work [], arithmetic averages of TL values over various frequency ranges were found to be good correlates of speech intelligibility scores. Table shows correlation coefficients of annoyance ratings of music sounds with arithmetic averages of TL values over frequency ranges from some lower frequency f to an upper frequency f. f f Table. Correlation coefficients between AA(f -f ) values and annoyance ratings of music sounds. The lowest included frequency is f (shown in left hand column) and the highest included frequency is f (top row). The shaded cells indicate values with magnitudes 0.90, and bold font 0.9. These same correlation coefficients are plotted on the contour map of Figure 8 illustrating how they vary with f and f. As the results at individual frequencies in Figure suggested, the IRC RR- - 8

21 strongest correlations with responses to music sounds occur when low or high frequencies are included f Hz f Hz Figure 8. Correlation coefficient between AA(f -f ) and annoyance ratings of music sounds (vertical axis lowest frequency f, horizontal axis upper frequency f ). f f Table. Correlation coefficient between AA(f -f ) values and annoyance ratings of speech sounds. The shaded cells indicate values with magnitudes 0.90, bold font 0.9. IRC RR- - 9

22 Table shows the results of correlating annoyance responses to speech sounds with arithmetic average transmission loss values, AA(f - f ), for a wide range of combinations of lower frequency f, and upper frequency f values. In contrast to responses to music sounds, for the annoyance ratings of speech sounds, the highest correlations were obtained when mid-to-high frequency TL values were included. The same correlation coefficients between annoyance ratings of transmitted speech sounds and AA(f - f ) values are plotted in the contour plot of Figure 9. A number of combinations yielded quite high correlation coefficients but simply including all /-octave bands from 00 to k Hz is a successful combination and includes all frequencies assessed in standard transmission tests. Alternatively, including the speech frequencies from 0 Hz to k Hz is equally successful. 80 f Hz f Hz Figure 9. Correlation coefficients between AA(f -f ) and annoyance ratings of speech sounds (vertical axis lowest frequency f, horizontal axis upper frequency f ). The pattern of correlation coefficients in Figures 8 and 9 for annoyance responses was found to be very similar to the correlations between the same arithmetic average transmission loss values and ness ratings. These are shown in Figures 0 and. The contours of correlation coefficient values for ness ratings of music sounds in Figure 0 are seen to be very similar to those in Figure 8 for annoyance ratings of music sounds. The results in Figure for ness ratings of speech sounds are very similar to those in Figure 9 for annoyance ratings of transmitted speech sounds. IRC RR- - 0

23 f, Hz f Hz Figure 0. Correlation coefficient between AA(f - f ) and ness ratings of music sounds (vertical axis lowest frequency f, horizontal axis upper frequency f ). IRC RR- -

24 80 f Hz f Hz Figure. Correlation coefficient between AA(f - f ) and ness ratings of speech sounds (vertical axis lowest frequency f, horizontal axis upper frequency f ).. Comparisons of some better Arithmetic Average TL measures versus AA(00-k) The results in Table suggest that an arithmetic average transmission loss over all of the frequencies normally included in transmission loss tests (AA(00-k)) would be strongly related to annoyance ratings of transmitted speech sounds. responses to both speech and music sounds are plotted versus AA(00-k) values in Figure. The annoyance ratings of speech sounds are strongly related to this measure with an associated R = 0.9. However, this measure was less well related to the annoyance ratings of music sounds (R =0.). IRC RR- -

25 AA(00,k), db Figure. Mean annoyance ratings of speech and music sounds versus AA(00-k). [, R =0.,, R =0.9] versus AA(00-.k) In the previous study [,] AA(00-.k) was best correlated with the speech intelligibility scores. When tested as a predictor of annoyance responses in the current work, this same measure was strongly related to annoyance ratings of speech responses (R = 0.89) but only weakly related to annoyance ratings of music ratings (R = 0.09)). A slightly different arithmetic average transmission loss measure, AA(00-.k), was found to be a better compromise that was better related to annoyance ratings of speech ratings as well as being almost equally well related to the speech intelligibility scores of the previous study. Figure plots annoyance responses versus AA(00-.k) values. ratings of speech responses were again strongly related with the AA(00-.k) values but annoyance ratings of music responses were not well predicted. The regression line for speech intelligibility scores from the previous study [,] is compared with the annoyance responses to speech sounds on Figure. The two regression lines have quite different slopes near their mid-points. That is, speech intelligibility scores vary more rapidly with AA(00-.k) values than do the ratings of annoyance ratings of speech. As a result, there is still reported annoyance when the intelligibility scores are close to 0 because listeners can hear the speech sounds even when the words are not intelligible. Another arithmetic average measure with a further expanded frequency range, AA(0-.k), was found to be a little better compromise for both speech intelligibility scores and annoyance ratings of speech sounds. When speech intelligibility scores were fitted to AA(0-.k) values with a Boltzmann equation, the related R was 0.9 and when annoyance ratings of speech sounds were considered, the associated R was 0.9. IRC RR- -

26 Intelligibility 00 0 SI, % AA(00-.k), db Figure. Mean annoyance ratings versus AA(00-.k) compared with speech intelligibility scores (right hand axis) from previous study [,], [, R =0.90,, R =0.89, intelligibility, R =0. 98]. versus AA(-0) and AA(-.k) The results in Table suggest that AA(-0) should be well correlated with the annoyance ratings of music responses. The results in Figure confirm that this measure was a good predictor of annoyance responses to music sounds and was also well related to annoyance ratings of speech sounds. By including a much broader range of frequencies, AA(-.k) better includes all aspects of the transmission loss versus frequency characteristics including both very low and very high frequencies. As the R values in the title of Figure indicate, this measure was quite well related to both annoyance ratings of speech and to music sounds. However, its success depended on the inclusion of frequencies that are not always included in standard sound transmission measurements AA(-0) Figure. Mean annoyance ratings of speech and music sounds versus AA(-0), [, R =0.99,, R =0.]. IRC RR- -

27 AA(-.k), db Figure. Mean annoyance ratings of speech and music sounds versus AA(-.k) values. [, R =0.88,, R =0.89] Loudness versus AA(00-.k) and AA(-.k) As shown in Figure, the arithmetic average transmission loss measure, AA(00-.k), was a good predictor of annoyance responses to speech sounds but was not so good for annoyance ratings of music responses. Figure shows similar relationships with this measure for ness ratings of transmitted speech and music sounds. The arithmetic average measure AA(-.k) with an extended frequency range is related to ness judgements in Figure. The results are similar to the plot of annoyance ratings versus this measure in Figure. Loudness 0 not audible AA(00-.k), db Figure. Mean ness ratings of speech and music sounds versus AA(00-.k) values, [, R =0.8,, R =0.8]. IRC RR- -

28 Loudness 0 not audible AA(-.k), db Figure. Mean ness ratings of speech and music sounds versus AA(-.k), [, R =0.,, R =0.99]. Table compares the results of the Boltzmann equation fits of the ness and annoyance responses with the more successful arithmetic average measures discussed above. As previously noted, some measures were better predictors of either responses to speech sounds or responses to music sounds but not responses to both types of sounds. As a compromise that predicts responses to both speech and music sounds, the AA(00-k) measure was reasonably successful. However, the two arithmetic average measures that included low frequencies, AA(-0) and AA(-.k), were also possible compromises for both speech and music sounds. The R values for annoyance responses were always very similar to those for the corresponding ness responses. Again ness and annoyance responses seem to convey the same information about attitudes to these sounds. Loudness Symbol Type f f R X 0 dx R X 0 dx AA(00-k) music 00 k speech 00 k AA(00-.k) music 00.k speech 00.k AA(00-.k) music 00.k speech 00.k AA(-0) music speech AA(-.k) music.k speech.k Table. Summary of regression results for better arithmetic average transmission loss measures. R values equal to or greater than 0.90 are shaded and R values equal to or greater than 0.9 are in bold font. IRC RR- -

29 . Effects of included frequencies for Energy Average TL measures Broadband transmission loss values were also created by energy averaging the measured transmission loss values over various frequency ranges rather than using arithmetic averages of these decibel values. The resulting correlation coefficients between these energy average transmission loss values and annoyance ratings of music responses are included in Table 8. In these results the lowest included frequency was varied from Hz to 000 Hz and the highest included frequency from 00 Hz to 00 Hz. f f Table 8. Correlation coefficients between energy average transmission loss values over various frequency ranges from f to f with annoyance ratings of music sounds. The shaded values indicate values with magnitudes 0.90, bold font 0.9. f f Table 9. Correlation coefficients between energy averaged transmission loss values over various frequency ranges from f to f with annoyance ratings of speech sounds. The shaded values indicate values with magnitudes 0.90, bold font 0.9. IRC RR- -

30 The highest correlation coefficient values in Table 8 were similar to those for the corresponding arithmetic average transmission loss values in Tables. However, the range of included frequencies that provided the strongest correlations were much wider than for the arithmetic averages in Table. For example, the results in Table 8 suggest that an energy average TL measure over the frequencies from 80 to k Hz would be a good predictor of annoyance ratings of music sounds. Table 9 shows the results of correlations between energy averaged transmission loss values for varied frequency range from f to f with annoyance ratings of speech sounds. The highest correlation coefficient values were a little lower than those for the corresponding arithmetic average transmission loss values in Table. IRC RR- - 8

31 . Variations of STC Ratings. Variations of the 8 db Rule In the previous study [,], that used speech intelligibility scores to rate sound insulation, removing the 8 db rule from the standard STC contour fitting procedure led to slightly improved predictions of intelligibility scores. Figure 8 shows the results of plotting annoyance responses versus STC values with the 8 db rule excluded. As the R values in the title of this figure indicate, removing the 8 db rule reduced the R values for annoyance ratings of music sounds but increased R for annoyance ratings of speech sounds STC no8 Figure 8. Mean annoyance ratings versus STC no8 values, [, R =0.0 (0.8),, R =0.90 (0.8)]. (Values in brackets are R with 8 db rule included). Figure 9 shows very similar results for ness ratings of speech and music sounds in terms of STC values calculated without an 8 db rule. As for the annoyance responses in Figure 8, removing the 8 db rule increased the R values for ness ratings of speech sounds but reduced them for ness rating of music sounds. Loudness 0 not audible STC no8 Figure 9. Mean ness ratings versus STC no8, [, R =0. (0.),, R =0.90 (0.88)]. (Values in brackets are R with 8 db rule included). IRC RR- - 9

32 The previous research [,] found that including the 8 db rule did not improve correlations with speech intelligibility scores and examined the benefits of varying the magnitude of the maximum allowed deviation from 8 db to other values. Similar variations in the allowed magnitude of the maximum allowed deviation were also examined in this research. Figure 0 shows the results of correlations of mean ness and annoyance ratings with STC values having varied maximum allowed deviation values. Figure 0 shows, that annoyance and ness ratings yielded very similar correlation coefficients. However response to music sounds and responses to speech sounds led to different results. For music, the lower the maximum acceptable deficiency was, the higher the resulting correlation coefficient. The inverse was true for speech; the higher the magnitude of the maximum acceptable deficiency, the higher the resulting correlation coefficient. For speech, the no 8 db rule case led to the highest correlation coefficients because this was similar to a very large maximum allowed deficiency. -.0 Correlation coefficient for music for speech Loudness for music Loudness for speech no Maximum deficiency, db Figure 0. Correlation coefficients of STC no8 values (for varied maximum allowed deficiency) with mean annoyance and ness ratings for speech and music sounds. Maximum Loudness Deficiency music speech music speech None Table 0. Correlation coefficients of STC no8 values (for varied maximum allowed deficiency) with mean annoyance and ness ratings for speech and music sounds. IRC RR- - 0

33 To better understand the results in Figure 0 and Table 0, the frequency at which the 8 db rule was applied was determined for all walls where the STC rating was limited by the 8 db rule. In all cases for which the 8 db rule was applied, it was applied at lower frequencies ( to 0 Hz) and most often (9 walls) in the Hz /-octave band. For the walls included in this study, the 8 db rule functioned to better represent the effects of low frequency dips in the TL versus frequency responses. Where there was a large dip and low frequency transmitted sounds could be unusually, the 8 db rule limited the STC value so that it better indicated the effect of the reduced attenuation of the low frequency sounds. For the music sounds with strong low frequency components, including the 8 db rule led to better sound insulation ratings that were better correlated with subjective ratings of the music sounds. However, for speech sounds without strong low frequency sound components, including the 8 db rule distorted the rating of the more important mid- and higher-frequency components of the transmitted speech sounds.. Variations of the Total Allowed Deviation The effect of varying the allowed total deviation in the STC and R w calculations was also examined by varying it from the db limit included in both the STC and R w procedures. This was done for annoyance and ness ratings of both speech and music sounds with and without the 8 db rule included. Figure plots the correlation of annoyance ratings for speech and music sounds versus the total allowed deviation used for cases with and without the 8 db rule. For speech without the 8 db rule, a maximum deficiency corresponding to the current standard value of db works as well as almost any value. If the maximum deficiency was much smaller than db, then the correlations with annoyance ratings of speech sounds decreased because the STC rating became more influenced by prominent low frequency dips in the transmission loss versus frequency characteristics. However, for annoyance ratings of music sounds, without the 8 db rule the opposite was true. The highest correlations occurred for the minimum total deficiency because then the prominent low frequency dips in the transmission loss versus frequency characteristics most influenced the STC rating. -.0 Correlation coefficient for music(no 8 db rule) for speech(no 8 db rule) Anoyance for music for speech Total deficiency, db Figure. Correlation coefficients between mean annoyance ratings and modified STC values for which the total allowed deficiency was varied from 0 to 0 db. When the 8 db rule was included, the results were a little different. For annoyance ratings of speech responses, the correlation coefficients increased until the total deficiency equalled 0 db. Presumably at higher values the STC became more influenced by the application of the 8 db rule because of prominent low frequency dips. Because these low frequency dips did not greatly IRC RR- -