汉语语境下的车辆噪声听觉属性评价与分析

研究了等响度车辆噪声听觉属性的空间维度及其汉语表述。首先通过问卷调查确定评价车辆噪声听觉属性的汉语描述词,然后完成基于成对比较法和语义细分法的主观评价实验,获得听觉属性的不相似度矩阵及描述词的等级顺序。最后,利用多维尺度分析和主成分分析法得到独立的用中文表达的车辆噪声听觉属性,结果表明:对于等响度的车辆噪声,其听觉属性可以用三个维度表示,分别用“粗糙”、“刺耳”和“起伏”描述。     

中西方典型乐器音高响度与音色明亮度的研究

为了客观地评价民族乐器与西洋乐器在听觉感知方面的差异,本文利用15种典型的中西方乐器声样本,建立了与音色、响度和音色明亮度有关的15种乐器的感知空间模型,通过这些模型可以预测不同乐器在音高、响度一定时,音色明亮度的感知情况。此外,根据已建立的感知空间模型分别对比弹拨乐器、拉弦乐器和不同类型的吹奏乐器中三种听觉感知属性的变化差异。结果表明,对于中西方典型乐器,音色明亮度随响度的增加而增大,但是响度对音色明亮度的影响程度受到音域和响度范围的影响。民族乐器的音色明亮度随音高的增加而增大,但是西洋乐器的音色明亮度并没有随音高的增加而发生明显的变化。     

动态水景声喜好度实验研究

采集不同地域环境中的动态水景声景观数据,采用里克特5级量表,通过实验室实验研究水景声喜好度与声音物理特征、水景形态与环境视觉喜好度和被试等因素的关系。分析发现:单听水景声音时,声喜好度与水景声音A声级显著负相关(r_p=-0.981,p<0.01),与瞬时变率显著正相关(r_p=0.720,p<0.05)。当谱质心与A声级和瞬时变率显著相关时,谱质心与声喜好度也显著负相关(r_p=-0.867,p<0.01);放映水景形态和环境默片,评价其视觉喜好度;将水景声音分别与形态、环境视频同时播放再评价其声喜好度,发现视觉喜好度较高的水景形态和环境能够明显提高大流速流量水景的声喜好度,视觉喜好度较低的水景形态和环境反而降低了小流速流量水景的声喜好度;男女生对水景声喜好度的评价有显著差异,本地学生对黑虎泉声喜好度评价比非本地学生高。因此,水景声喜好度受其声音物理指标、形态和环境视觉因素及被试自身等因素的影响。     

头相关传输函数幅度谱的听觉空间分辨阈值的分析

提出一种分析头相关传输函数(head-related transfer function,HRTF)幅度谱的听觉空间分辨阈值模型。采用数值计算得到的高空间分辨率HRTF数据,计算了声源空间位置变化引起的HRTF幅度谱的变化,进一步利用Moore响度模型分析双耳响度级差、双耳响度级谱和总响度级等三个听觉感知量的变化。根据现有的3个听觉感知量最小可察觉差异,模型利用双耳响度级差和双耳响度级谱的变化得到的估计结果与心理声学实验一致,因此是一种有效预测听觉空间分辨阈值的方法,可用于为简化虚拟听觉信号处理和数据储存。     

头相关传输函数幅度谱的听觉空间分辨阈值的分析

提出一种分析头相关传输函数(head-related transfer function,HRTF)幅度谱的听觉空间分辨阈值模型。采用数值计算得到的高空间分辨率HRTF数据,计算了声源空间位置变化引起的HRTF幅度谱的变化,进一步利用Moore响度模型分析双耳响度级差、双耳响度级谱和总响度级等三个听觉感知量的变化。根据现有的3个听觉感知量最小可察觉差异,模型利用双耳响度级差和双耳响度级谱的变化得到的估计结果与心理声学实验一致,因此是一种有效预测听觉空间分辨阈值的方法,可用于为简化虚拟听觉信号处理和数据储存。     

复杂谐波信号中响度和谱质心的相互影响——响度和谱质心辨别阈的实验研究

根据阈值测量实验和变化一致性测量实验,在单自变量干扰和双自变量干扰两种情况下量化分析响度和谱质心之间的交互关系。研究发现:(1)复杂谐波信号中谱质心对响度的影响强于响度对谱质心的影响;(2)声音的变化方向对被试判断响度和谱质心的阈值存在影响。研究结果进一步呈现了响度和谱质心的定量特征,为听觉属性交互关系的深入研究提供了新的思路。     

采用生理响度感知机制的车内噪声声品质预测

现有车内噪声声品质预测的响度计算中没有考虑真实人耳生理解剖结构的传声特性,因此提出了一种基于生理响度感知模型的车内噪声声品质预估方法。首先,采集两款轿车的车内噪声样本,并通过主观评价试验得到车内噪声的主观评价烦恼度。之后,整合中耳集总参数模型与耳蜗传输线模型,构建生理响度模型。然后,以生理响度模型的响度计算值为主要参数,结合尖锐度、粗糙度与车内噪声的主观评价值,通过TabNet模型构建了车辆声品质预测模型。最终,对比分析了所构建声品质模型与基于现有标准响度模型所构建的声品质模型的预测效果。结果表明,采用生理响度模型的声品质预测平均误差百分比仅有4.73%,优于采用Moore响度(6.13%)与Zwicker响度(6.94%)的声品质预测结果。此外,所构建的TabNet声品质预测模型的平均误差百分比也低于基于BP神经网络模型的平均误差百分比(7.60%)。采用生理响度模型的TabNet声品质预测能够提高车内噪声声品质客观评价的准确率。     

关于音乐厅响度评价的研究

该文回顾并综述了对音乐厅(包括西洋交响乐厅及中国民族音乐厅)响度评价的研究历程,指出采用乐队齐奏强音标志乐段的平均声压级L_pF作为评价音乐厅响度客观指标的合理性与可行性。文中给出L_pF的计算方法以及对若干厅堂计算值与实测值的比较,并通过主观评价,给出L_pF的初步优选值域。采用L_pF作为响度评价指标的好处不仅在于它能表征听众听到的绝对响度的感受,还在于能预判何种规模的乐队适于在多大规模的音乐厅中演出,以便达到较佳响度效果的问题。     

Topcolor Assisted Technicolor Models and Single Top Quark Production via at the Tevatron

［1］C.T. Hill, Phys. Lett. B345 (1995) 483; K. Lane and E.Eichten, Phys. Lett. B352 (1995) 382; K. Lane, Phys.Lett. B433 (1998) 96. ［2］R. Raja, presented at the XXXII Rencontres de Moriond on Electroweak Interactions and Unified Theories, les Arcs, Savoie, France, March 15-22, (1997). ［3］M.E. Peskin, “Physics and Experiments with Linear Collider“, Proceedings of the Workshop, Saarilka, Finland (1991), eds R. Orava, P. Eerala and M. Nordberg, World Scientific, Singapore (1992); A.P. Heinson, Talk given at the XXXIst Rencontres de Moriond, “QCD and High Energy Hadronic Interactions“, les Arcs, Savoie, France,23rd-30th March (1996), Fermilab-Conf. 96/116-E, May (1996). ［4］R.D. Peccei and X. Zhang, Nucl. Phys. B337 (1990) 269;R.D. Peccei, S. Peris and X. Zhang, Nucl. Phys. B349(1991) 305. ［5］S. Dawson, Nucl. Phys. B249 (1985) 42; S. Willenbrock and D. Dicus, Phys. Rev. D34 (1986) 155; S. Dawson and S. Willenbrock, Nucl. Phys. B284 (1987) 449; C.-P.Yuan, Phys. Rev. D41 (1990) 42; F. Anselmo, B. van Eijk and G. Bordes, Phys. Rev. D45 (1992) 2312; R.K. Ellis and S. Parlce, Phys. Rev. D46 (1992) 3875; D. Carlson and C.-P. Yuan, Phys. Lett. B306 (1993) 386; G. Bordes and B. van Eijk, Nucl. Phys. B435 (1995) 23; A. Heinson,A. Belyaev and E. Boos, Phys. Rev. D56 (1997) 3114. ［6］S. Cortese and R. Petronzio, Phys. Lett. B306 (1993) 386;T. Stelzer and S. Willenbrock, Phys. Lett. B357 (1995)125. ［7］M. Smith and S. Willenbrock, Phys. Rev. 954 (1996)6696. ［8］T.G. Rizzo, Phys. Rev. D53 (1996) 6218; G. Mahlon and S. Parke, Phys. Rev. D55 (1997) 7249. ［9］E.H. Simmons, Phys. Rev. D55 (1997) 5494. ［10］A. Datta and X. Zhang, Phys. Rev. D55 (1997) 2530. ［11］YUE Chong-Xing, KUANG Yu-Ping and LU Gong-Ru,Phys. Rev. D56 (1997) 291. ［12］G. Buchalla, G. Burdman, C.T. Hill and D. Kominis,Phys. Rev. D53 (1996) 5185. ［13］K. Lane, Phys. Lett. B357 (1995) 624; YUE ChongXing, ZHOU Hong-Yi, KUANG Yu-Ping and LU GongRu, Phys. Rev. D55 (1997) 5541. ［14］L. Randall and E.H. Simmons, Nucl. Phys. B3S0 (1992)3; V. Lubicz, Nucl. Phys. B404 (1993) 559; V. Lubicz and P. Santorclli, Nucl. Phys. B460 (1996) 3. ［15］G.H. WU, Phys. Rev. Lett. 74 (1995) 4173; C.X. YUE,Y.P. KUANG, et al., Phys. Rev. D52 (1995) 5314; K.Hagiwara and N. Kitazawa, Phys. Rev. D52 (1995) 5374. ［16］C.X. YUE, Y.P. KUANG and G.R. LU, J. Phys. G23(1997) 163. ［17］W. Loinaz and T. Takuchi, Phys. Rev. D60 (1999)015005. ［18］M.B. Popovic and E.H. Simmons, Phys. Rev. D58 (1998)095007. ［19］B. Balaji, Phys. Rev. D53 (1996) 1699. ［20］K. Eicbten and K. Lane, Phys. Lett. B222 (1989) 129; K.Lane and M.V. Ramana, Phys. Rev. D44 (1991) 2678. ［21］Z.J. XIAO, L.D. WAN, G.R. LU, J.M. YANG, X.L.WANG, L.B. GAO and C.X. YUE, J. Phys. G20 (1994)901. ［22］G. Burdman and D. Kominis, Phys. Lett. B403 (1997)101. ［23］C.X. YUE, Y.P. KUANG, X.L. WANG and W.B. LI, hepph/0001133, Phys. Rev. D62 (2000) 055005. ［24］J.H. Field, UGVA-DPNC (120-173) hep-ph/9801413(1997); D. Chang and E. Ma, hep-ph/9909537. ［25］A.P. Heinson, “Future Top Physics at the Tevatron and LHC“, hep-ex/9605010; A.P. Heinson, A.S. Belayev and E.E. Boos, Phys. Rev. D56 (1997) 3114; M. Bohm, W.Hollik and H. Spiesbergerm, Fortschr. Phys. 34 (1986)687. ［26］G.R. LU, et al., Phys. Rev. D54 (1996) 1083. ［27］J. Morfin and W.K. Tung, Z. Phys. C52 (1991) 13. ［28］A. Axelrod, Nucl. Phys. B209 (1982) 349; G. Passarino and M. Veltman, ibid. B160 (1979) 151; M. Clements, et al., Phys. Rev. D27 (1983) 570.     

感知线性预测在水下目标分类中的应用研究

提出了基于感知线性预测(PLP)的模仿人耳听觉特性来提取水声信号鲁棒特征的方法。运用听觉心理学的三个概念: (1)临界带谱分析、(2)等响度曲线、(3)强度响度听觉幂率,形成估计听觉谱的方法,可获得一个12阶全极点模型的鲁棒特征矢量。运用这一特征矢量进行训练和识别的实验结果表明:(1)在不同的频率段内,人耳对6类目标辐射噪声信号敏感程度是不同的。(2)提取的基于听觉感知水下目标特征具有鲁棒性。(3)通过此方法提取的特征维数较低,运算速度快,识别的正确率比以往有所提高。     

Automatic speech recognition using psychoacoustic models.

An approach to automatic speech recognition is described, which, in a straightforward way, follows the concept of (1) preprocessing in terms of auditory parameters and (2) subsequent classification and recognition. The preprocessing system has been realized in analog hardware, while recognition is carried out on a digital computer. In the preprocessing system, the essential psychoacoustic principles of the perception of loudness, pitch, roughness, and subjective duration are implemented with some approximation. The system essentially consists of 24 bandpass filters, nonlinear transformation of each filter output into specific loudness and specific roughness, and final transformation of these parameters into total loudness, total roughness, and three spectral momenta. As a means to further reduce the information flow, continuous selection of dominant parameters is also considered on the basis of psychoacoustic data. The subsequent recognition process is mainly characterized by (1) discrimination between speech and silent periods, (2) detection of syllable peaks and classification of syllable nuclei, and (3) assumption of syllable boundaries and classification of consonant clusters. Though the entire system as yet is far from being complete and perfect, the present results indicate that the concept provides a systematic and promising way towards automatic recognition of continuous speech.     

Memory for pitch versus memory for loudness

The decays of pitch traces and loudness traces in short-term auditory memory were compared in forced-choice discrimination experiments. The two stimuli presented on each trial were separated by a variable delay (D); they consisted of pure tones, series of resolved harmonics, or series of unresolved harmonics mixed with lowpass noise. A roving procedure was employed in order to minimize the influence of context coding. During an initial phase of each experiment, frequency and intensity discrimination thresholds [P(C) = 0.80] were measured with an adaptive staircase method while D was fixed at 0.5 s. The corresponding physical differences (in cents or dB) were then constantly presented at four values of D: 0.5, 2, 5, and 10 s. In the case of intensity discrimination, performance (d') markedly decreased when D increased from 0.5 to 2 s, but was not further reduced when D was longer. In the case of frequency discrimination, the decline of performance as a function of D was significantly less abrupt. This divergence suggests that pitch and loudness are processed in separate modules of auditory memory.     

Characterizing the sound quality of air-conditioning noise

The aim of the psychoacoustic study presented here was to characterize listeners' preferences for a set of sounds produced by different brands and models of indoor air-conditioning units. In addition, some synthetic sounds, created by interpolation between recorded sound samples, were integrated into the set. The multidimensional perceptual space and the corresponding physical space representative of the sound set were determined with multidimensional scaling (MDS). Then the preferences for different classes of listeners were related to the physical space. The best spatial model yielded by the MDS had three common dimensions and specificities. The three dimensions are correlated with the ratio of the noisy part of the spectrum to the harmonic part (NHR), with the spectral center of gravity (SC) and with loudness (N). Two classes of listeners can be distinguished in terms of preference. For one, preference varied primarily with loudness, whereas for the other it varied more with SC and NHR. However, for one class the preference grew with the parameter NHR, while it decreased for the other class. The results replicate under different laboratory conditions and indicate the usefulness of this sound quality assessment approach for characterizing appliance noises.     

Perceptual interactions in the loudness of combined auditory and vibrotactile stimuli

The loudness of auditory (A), tactile (T), and auditory-tactile (A+T) stimuli was measured at supra-threshold levels. Auditory stimuli were pure tones presented binaurally through headphones; tactile stimuli were sinusoids delivered through a single-channel vibrator to the left middle fingertip. All stimuli were presented together with a broadband auditory noise. The A and T stimuli were presented at levels that were matched in loudness to that of the 200-Hz auditory tone at 25 dB sensation level. The 200-Hz auditory tone was then matched in loudness to various combinations of auditory and tactile stimuli (A+T), and purely auditory stimuli (A+A). The results indicate that the matched intensity of the 200-Hz auditory tone is less when the A+T and A+A stimuli are close together in frequency than when they are separated by an octave or more. This suggests that A+T integration may operate in a manner similar to that found in auditory critical band studies, further supporting a strong frequency relationship between the auditory and somatosensory systems.     

Functional magnetic resonance imaging measurements of sound-level encoding in the absence of background scanner noise

Effects of sound level on auditory cortical activation are seen in neuroimaging data. However, factors such as the cortical response to the intense ambient scanner noise and to the bandwidth of the acoustic stimuli will both confound precise quantification and interpretation of such sound-level effects. The present study used temporally "sparse" imaging to reduce effects of scanner noise. To achieve control for stimulus bandwidth, three schemes were compared for sound-level matching across bandwidth: component level, root-mean-square power and loudness. The calculation of the loudness match was based on the model reported by Moore and Glasberg [Acta Acust. 82, 335-345 (1996)]. Ten normally hearing volunteers were scanned using functional magnetic resonance imaging (tMRI) while listening to a 300-Hz tone presented at six different sound levels between 66 and 91 dB SPL and a harmonic-complex tone (F0= 186 Hz) presented at 65 and 85 dB SPL. This range of sound levels encompassed all three bases of sound-level matching. Activation in the superior temporal gyrus, induced by each of the eight tone conditions relative to a quiet baseline condition, was quantified as to extent and magnitude. Sound level had a small, but significant, effect on the extent of activation for the pure tone, but not for the harmonic-complex tone, while it had a significant effect on the response magnitude for both types of stimulus. Response magnitude increased linearly as a function of sound level for the full range of levels for the pure tone. The harmonic-complex tone produced greater activation than the pure tone, irrespective of the matching scheme for sound level, indicating that bandwidth had a greater effect on the pattern of auditory activation than sound level. Nevertheless, when the data were collapsed across stimulus class, extent and magnitude were significantly correlated with the loudness scale (measured in phons), but not with the intensity scale (measured in SPL). We therefore recommend the loudness formula as the most appropriate basis of matching sound level to control for loudness effects when cortical responses to other stimulus attributes, such as stimulus class, are the principal concern.     

Dependence of binaural loudness summation on interaural level differences, spectral distribution, and temporal distribution

Loudness of interaurally correlated narrow- and broadband noises was investigated using a loudness estimation paradigm (with two anchors) presented via headphones. Throughout the experiments (most performed by 12 subjects), the results from both anchors agreed very well. In the first experiment, third-octave-band noises centered around 250, 710, or 2000 Hz, as well as broadband red (-10 dB/oct), pink (-3 dB/oct), and blue (+10 dB/oct) noises, with interaural level differences of delta L = 0, 4, 10, 20, and infinity dB, were presented as test signals while the same sound presented monaurally or diotically served as anchor. The binaurally summed loudness at delta L = 0 dB was found to be larger than the loudness of a monaural signal of the same SPL by a factor of about 1.5 and decreased with increasing delta L. No dependence of this behavior on frequency, level, or spectral shape was found. In a second experiment, abutting frequency bands of varying width were alternately presented to the subject's left and right ears with the overall spectrum encompassing the whole audio range. The binaural loudness was larger for fewer but broader frequency bands. In a third experiment, uniform exciting noise was switched between the two ears at various speeds. Increasing the switching frequency gave rise to an increase in loudness of about 20%. All results are discussed from the viewpoint of the use of the standardized loudness meter. At this point, there is no evidence that any significant systematic errors due to single-channel evaluation (in contrast to the human two-channel processing) are made by measuring loudness using these meters.     

Characteristics of driven polymer surfaces: growth and roughness

Using a Monte Carlo simulation, the growth and roughness characteristics of polymer surfaces are studied in 2+1 dimensions. Kink-jump and reptation dynamics are used to move polymer chains under a driving field where they deposit onto an impenetrable attractive wall. Effects of field (E), chain length (L(c)), and the substrate size (L) on the growing surfaces are studied. In low field, the interface width (W) shows a crossover from one power-law growth in time (W approximately t(beta(1))) to another (W approximately t(beta(2))), before reaching its asymptotic value (W(s)), with beta(1)( approximately 0.5+/-0.1)相似文献   

A-weighted sound pressure level as a loudness/annoyance indicator for environmental sounds - Could it be improved?

A system is introduced with the purpose of showing how an auditory perception system may be built up to include the basic quantities on loudness domain. The quantities are the critical bands, the power law, and the weighting. The power law seems to be the most crucial basis for hypothesizing a loudness function. It has been shown that the power law could be applied as such by assuming the auditory perception system to have two essentially different stimuli: the intensity (sound pressure level) and pure pressure. These physically different quantities seem to be combined in the root of the power law, and in this study the roots are determined from equal-loudness contours. A loudness function is derived on the basis of this finding. By adding the weighting, a method has been constructed for assessing loudness. After defining the weighting, the equal-loudness contours are constructed and are seen to be virtually identical to the contours in ISO 226. It has also been found that the equations for deriving the contours in this standard and in the new ISO 226 may be incorrect, because there is no definition of a sensible loudness function. Finally, it is deduced that the derived weighting must be unequivocal for an auditory perception system (depending solely on the otologically representative group). Finally, the A-weighting (as part of an A-weighted sound pressure level) as such is reasonably similar to the weighting derived in this study. Therefore, this weighting is not the main problem when assessing sounds in respect to loudness. The A-weighting is thus chosen as the weighting for the indicator derived in the study for assessing environmental sounds.     

Comparison of the Produced and Perceived Voice Range Profiles in Untrained and Trained Classical Singers

Frequency and intensity ranges (in true decibel sound pressure level, 20 microPa at 1 m) of voice production in trained and untrained vocalists were compared with the perceived dynamic range (phons) and units of loudness (sones) of the ear. Results were reported in terms of standard voice range profiles (VRPs), perceived VRPs (as predicted by accepted measures of auditory sensitivities), and a new metric labeled as an overall perceptual level construct. Trained classical singers made use of the most sensitive part of the hearing range (around 3-4 kHz) through the use of the singer's formant. When mapped onto the contours of equal loudness (depicting nonuniform spectral and dynamic sensitivities of the auditory system), the formant is perceived at an even higher sound level, as measured in phons, than a flat or A-weighted spectrum would indicate. The contributions of effects like the singer's formant and the sensitivities of the auditory system helped the trained singers produce 20% to 40% more units of loudness, as measured in sones, than the untrained singers. Trained male vocalists had a maximum overall perceptual level construct that was 40% higher than the untrained male vocalists. Although the A-weighted spectrum (commonly used in VRP measurement) is a reasonable first-order approximation of auditory sensitivities, it misrepresents the most salient part of the sensitivities (where the singer's formant is found) by nearly 10 dB.