Stochastic Signal Processing for Sound Environment System with Decibel Evaluation and Energy Observation

Abstract

In real sound environment system, a specific signal shows various types of probability distribution, and the observation data are usually contaminated by external noise (e.g., background noise) of non-Gaussian distribution type. Furthermore, there potentially exist various nonlinear correlations in addition to the linear correlation between input and output time series. Consequently, often the system input and output relationship in the real phenomenon cannot be represented by a simple model using only the linear correlation and lower order statistics. In this study, complex sound environment systems difficult to analyze by using usual structural method are considered. By introducing an estimation method of the system parameters reflecting correlation information for conditional probability distribution under existence of the external noise, a prediction method of output response probability for sound environment systems is theoretically proposed in a suitable form for the additive property of energy variable and the evaluation in decibel scale. The effectiveness of the proposed stochastic signal processing method is experimentally confirmed by applying it to the observed data in sound environment systems.

1. Introduction

A specific signal in real sound environment system usually exhibits multifarious and complex characteristics such as non-Gaussian distribution and nonlinear property relating to natural, social, or human factors. Furthermore, the observation data usually are contaminated by external noise (e.g., background noise) with complex statistical properties. In this situation, in order to evaluate the sound environment system, precise estimation of the system characteristics of the sound environment is required by considering the contaminated observation data.

Furthermore, the internal physical mechanism of the real sound environment system is often difficult to recognize analytically, and it contains unknown structural characteristics. In our previous study, it was found that complex sound environment systems are difficult to analyze by using usual structural methods based on the physical mechanism [1]. Therefore, a nonlinear system model was derived in the expansion series form reflecting various types of correlation information from the lower order to the higher order between input and output variables [2]. The conditional probability density function contains the linear and nonlinear correlations in the expansion coefficients and these correlations play an important role as the statistical information for the input and output relationship of sound environment system.

On the other hand, in considering the relationship between the evaluation from top-down viewpoint and the countermeasure from bottom-up viewpoint in the sound environment system, noise evaluation quantities in decibel scale like 𝐿_𝑥 ((100 −  𝑥) percentile level) and 𝐿_Aeq (averaged energy on decibel scale) and some countermeasure methods in energy scale are widely used. Since there is a certain scale transform between decibel and energy variables, a unified general consideration without losing their mutual relationship has to be derived.

In this study, a general type of complex sound environment systems is considered. A stochastic signal processing method for predicting the output response probability distribution in decibel scale based on the input observations is proposed for complex sound environment systems. More specifically, an expansion expression of the conditional probability distribution in decibel scale is adopted as the system characteristics. Next, a method to estimate the system parameters reflecting several orders of correlation information between the input and output variables is derived by considering the additive property of energy variables under existence of external noise. Furthermore, a prediction method for the output probability distribution in decibel scale is also considered.

The effectiveness of the proposed theory is confirmed experimentally by applying it to real data of a sound insulation system and the road traffic noise environment measured around a national road in Hiroshima city.

2. Evaluation of Sound Environment System under Existence of External Noise

2.1. Statistical Model for Sound Environment System

Let 𝑋 and 𝑌 be the sound pressure levels of input and output signals for a complex sound environment system. The probability distribution of output 𝑌 has to be predicted on the basis of the observed data of the input level 𝑋, because noise evaluation quantities connected with probability distribution are widely used. All the information on linear and/or nonlinear correlations between 𝑋 and 𝑌 is included in the conditional probability density function 𝑃(𝑌∣𝑋) [2].

In order to find explicitly the various correlation properties between 𝑋 and 𝑌, let us expand the joint probability density function 𝑃(𝑋,𝑌) into an orthogonal polynomial series [3], as follows: 𝑃(𝑋,𝑌)=𝑃₀(𝑋)𝑃₀(𝑌)^∞∞_𝑟=0𝑠=0𝐴_𝑟𝑠𝜑_𝑟⁽¹⁾(𝑋)𝜑_𝑠⁽²⁾(𝑌),𝐴_𝑟𝑠≡𝜑_𝑟⁽¹⁾(𝑋)𝜑_𝑠⁽²⁾(𝑌), (1) where ⟨⟩ denotes the averaging operation. 𝑃₀(𝑋) and 𝑃₀(𝑌) can be chosen arbitrarily as the probability density functions describing the dominant parts of the actual fluctuation pattern. Two functions 𝜑_𝑟⁽¹⁾(𝑋) and 𝜑_𝑠⁽²⁾(𝑌) are orthogonal polynomials with the weighting functions 𝑃₀(𝑋) and 𝑃₀(𝑌). The information on the various types of linear and/or nonlinear correlations between 𝑋 and 𝑌 is reflected hierarchically in each expansion coefficient 𝐴_𝑟𝑠. In this section, the Gaussian distribution suitable for the random variables in decibel scale is adopted as 𝑃₀(𝑋) and 𝑃₀(𝑌)𝑃₀(𝑋)=𝑁𝑋;𝜇_𝑋,𝜎²_𝑋,𝑃₀(𝑌)=𝑁𝑌;𝜇_𝑌,𝜎²_𝑌 (2) with 𝑁≡1𝑥;𝜇,𝜎√2𝜋𝜎²𝑒^{−(𝑥−𝜇)2/2𝜎2},𝜇_𝑋≡⟨𝑋⟩,𝜎²_𝑋≡𝑋−𝜇_𝑋²,𝜇_𝑌≡⟨𝑌⟩,𝜎²_𝑌≡𝑌−𝜇_𝑌². (3) Thus, orthogonal polynomials 𝜑_𝑟⁽¹⁾(𝑋) and 𝜑_𝑠⁽²⁾(𝑌) are given by the Hermite polynomial [3]: 𝜑_𝑟⁽¹⁾(𝑋)=1√𝐻𝑟!^𝑟𝑋−𝜇_𝑋𝜎_𝑋,𝜑_𝑠⁽²⁾(𝑌)=1√𝐻𝑠!^𝑠𝑌−𝜇_𝑌𝜎_𝑌. (4) Substituting (1) into the definition of the conditional probability, 𝑃(𝑌∣𝑋) can be expressed in an expansion series form as follows: 𝑃()=𝑃()𝑌∣𝑋𝑋,𝑌𝑃(𝑋)=𝑃₀(𝑌)∑^∞_𝑟=0∑^∞_𝑠=0𝐴_𝑟𝑠𝜑_𝑟⁽¹⁾(𝑋)𝜑_𝑠⁽²⁾(𝑌)∑^∞_𝑟=0𝐴_𝑟0𝜑_𝑟⁽¹⁾(𝑋). (5)

2.2. Estimation of Correlation Information Based on Energy Observation

In the measurement of the sound environment, the effects by external noise (e.g., background noise) are inevitable. Then, based on the additive property of energy variable, the observed sound intensity 𝑧_𝑘 at a discrete time 𝑘 is expressed as 𝑧_𝑘=𝑦_𝑘+𝑣_𝑘, (6) where 𝑦_𝑘 and 𝑣_𝑘 are sound intensities of the output signal for the sound environment system and external noise. We assume that the statistics of the external noise are known. In this section, an estimation method for the expansion coefficients 𝐴_𝑟𝑠 in (1), reflecting the correlation information between 𝑋 and 𝑌, is derived on the basis of the observed data 𝑧_𝑘. There are relationships between energy variables 𝑥_𝑘, 𝑦_𝑘 and decibel variables 𝑋_𝑘, 𝑌_𝑘 for the input and output signals, as 𝑋_𝑘=10log₁₀𝑥_𝑘𝐸₀[dB],𝑌_𝑘=10log₁₀𝑦_𝑘𝐸₀[],𝐸dB₀=10⁻¹²W/m². (7) Therefore, from (7), the following relationships can be obtained 𝑥_𝑘=𝑒^{(𝑋𝑘−𝐾)/𝐶},𝑦_𝑘=𝑒^{(𝑌𝑘−𝐾)/𝐶} (8) with 𝐶≡10ln10,𝐾≡−𝐶ln𝐸₀. (9)

Next, considering the expansion coefficients 𝐴_𝑟𝑠 as unknown parameter vector 𝐚, 𝐚≡𝑎₁,𝑎₂,𝑎₃,…≡𝐚₍₁₎,𝐚₍₂₎,𝐚,…_(𝑠)≡𝐴_0𝑠,𝐴_1𝑠,𝐴_2𝑠…,(𝑠=1,2,…), (10) the simple dynamical model, 𝐚_𝑘+1=𝐚_𝑘,𝐚_𝑘≡𝑎_1,𝑘,𝑎_2,𝑘,𝑎_3,𝑘,…≡𝐚_(1),𝑘,𝐚_(2),𝑘,…, (11) is naturally introduced for the successive estimation of the parameter.

In order to derive the estimation algorithm of the parameter, attention is focused on Bayes’ theorem for the conditional probability distribution: 𝑃𝐚_𝑘∣𝑍_𝑘=𝑃𝐚_𝑘,𝑧_𝑘∣𝑍_𝑘−1𝑃𝑧_𝑘∣𝑍_𝑘−1, (12) where 𝑍_𝑘≡{𝑧₁,𝑧₂,…,𝑧_𝑘} is a set of observation data up to time 𝑘. Based on (12), using the similar calculation process to the previously reported paper [4], the estimate for an arbitrary polynomial function 𝑓_𝐌(𝐚_𝑘) of 𝐚_𝑘 with 𝐌th order can be derived as follows (cf. Appendix): 𝑓_𝐌𝐚_𝑘≡𝑓_𝐌𝐚_𝑘∣𝑍_𝑘=∑^𝐌_𝐦=𝟎∑^∞_𝑛=0𝐵_𝐦𝑛𝐶_𝐌𝐦𝜃_𝑛⁽²⁾𝑧_𝑘∑^∞_𝑛=0𝐵_𝟎𝑛𝜃_𝑛⁽²⁾𝑧_𝑘, (13) with 𝐵_𝐦𝑛≡𝜃_𝐦⁽¹⁾𝐚_𝑘𝜃_𝑛⁽²⁾𝑧_𝑘∣𝑍_𝑘−1,𝐦≡𝑚₁,𝑚₂,…. (14) Two functions 𝜃_𝐦⁽¹⁾(𝐚_𝑘) and 𝜃_𝑛⁽²⁾(𝑧_𝑘) are orthonormal polynomials with the weighting functions 𝑃₀(𝐚_𝑘∣𝑍_𝑘−1) and 𝑃₀(𝑧_𝑘∣𝑍_𝑘−1). Furthermore, 𝐶_𝐌𝐦 is the coefficient when the function 𝑓_𝐌(𝐚_𝑘) is expanded as 𝑓_𝐌𝐚_𝑘=^𝐌_𝐦=𝟎𝐶_𝐌𝐦𝜃_𝐦⁽¹⁾𝐚_𝑘. (15)

As the concrete expression on the fundamental probability function for the parameter 𝐚_𝑘 fluctuating in both positive and negative range, a standard Gaussian distribution is adopted. Furthermore, a gamma distribution is adopted as the probability function for the sound intensity 𝑧_𝑘: 𝑃₀𝐚_𝑘∣𝑍_𝑘−1=_𝑖𝑁𝑎_𝑖,𝑘;𝑎^∗_𝑖,𝑘,Γ_𝑖,𝑘,𝑃₀𝑧_𝑘∣𝑍_𝑘−1=𝑃_Γ𝑧_𝑘;𝑚^∗_𝑘,𝑠^∗_𝑘, (16) with 𝑃_Γ()≡𝑥𝑥;𝑚,𝑠^𝑚−1Γ(𝑚)𝑠^𝑚𝑒^−𝑥/𝑠,𝑎^∗_𝑖,𝑘≡𝑎_𝑖,𝑘∣𝑍_𝑘−1,Γ_𝑖,𝑘≡𝑎_𝑖,𝑘−𝑎^∗_𝑖,𝑘²∣𝑍_𝑘−1,𝑚^∗_𝑘≡𝑧^∗_𝑘²Ω_𝑘,𝑠^∗_𝑘≡Ω_𝑘𝑧^∗_𝑘,𝑧^∗_𝑘≡𝑧_𝑘∣𝑍_𝑘−1,Ω_𝑘≡𝑧_𝑘−𝑧^∗_𝑘²∣𝑍_𝑘−1. (17) Therefore, the orthogonal polynomials with the weighting functions of (16) are given by Hermite polynomial and Laguerre polynomial [3]: 𝜃_𝐦⁽¹⁾𝐚_𝑘=_𝑖1𝑚_𝑖!𝐻_𝑚𝑖𝑎_𝑖,𝑘−𝑎^∗_𝑖,𝑘Γ_𝑖,𝑘,𝜃_𝑛⁽²⁾𝑧_𝑘=Γ𝑚^∗_𝑘𝑛!Γ𝑚^∗_𝑘𝐿+𝑛^{(𝑚∗𝑘𝑛−1)}𝑧_𝑘𝑠^∗_𝑘. (18) By considering (6) and independence of 𝑦_𝑘 and 𝑣_𝑘, two parameters 𝑧^∗_𝑘 and Ω_𝑘 in (17) can be given by 𝑧^∗_𝑘=𝑦^∗_𝑘+𝑣_𝑘,𝑦^∗_𝑘≡𝑦_𝑘∣𝑍_𝑘−1,Ω_𝑘=𝑦_𝑘−𝑦^∗_𝑘²∣𝑍_𝑘−1+𝑣_𝑘−𝑣_𝑘². (19) Considering (5) and the property of conditional expectation, the first terms of the right sides in the above equations are expressed as follows: 𝑦_𝑘∣𝑍_𝑘−1=𝑦_𝑘∣𝑥_𝑘,𝑍_𝑘−1∣𝑍_𝑘−1=^∞₀𝑦_𝑘𝑃𝑦_𝑘∣𝑥_𝑘𝑑𝑦_𝑘∣𝑍_𝑘−1,𝑦_𝑘−𝑦^∗_𝑘²∣𝑍_𝑘−1=^∞₀𝑦²_𝑘𝑃𝑦_𝑘∣𝑥_𝑘𝑑𝑦_𝑘∣𝑍_𝑘−1−𝑦^∗_𝑘². (20) The integrals in (20) can be calculated by using the relationship between energy and decibel variables in (8) and expansion expression in (1), as follows: 𝐼_𝑙≡^∞₀𝑦^𝑙_𝑘𝑃𝑦_𝑘∣𝑥_𝑘𝑑𝑦_𝑘=^∞_−∞𝑒^{𝑙(𝑌𝑘−𝐾)/𝐶}𝑃𝑌_𝑘∣𝑋_𝑘𝑑𝑌_𝑘=^∞_−∞𝑒^{𝑙(𝑌𝑘−𝐾)/𝐶}𝑃₀𝑌_𝑘×^∞∞_𝑟=0𝑠=0𝐴_𝑟𝑠𝜑_𝑟⁽¹⁾𝑋_𝑘1√𝐻𝑠!^𝑠𝑌_𝑘−𝜇_𝑌𝜎_𝑌𝑑𝑌_𝑘×^∞_𝑟=0𝐴_𝑟0𝜑_𝑟⁽¹⁾𝑋_𝑘⁻¹=exp𝐶𝜇_𝑌+𝑙𝜎²_𝑌²2𝜎²_𝑌𝐶²−𝐶𝜇²_𝑌+2𝑙𝜎²_𝑌𝐾2𝜎²_𝑌𝐶×^∞_−∞𝑁𝑌_𝑘;𝜉_𝑘,𝜎²_𝑌×^∞∞_𝑟=0𝑠=0𝐴_𝑟𝑠𝜑_𝑟⁽¹⁾𝑋_𝑘1√𝐻𝑠!^𝑠𝑌_𝑘−𝜇_𝑌𝜎_𝑌𝑑𝑌_𝑘×^∞_𝑟=0𝐴_𝑟0𝜑_𝑟⁽¹⁾𝑋_𝑘⁻¹, (21) where 𝜉_𝑘≡1𝐶𝐶𝜇_𝑌+𝑙𝜎²_𝑌, (22) and 𝑑^(𝑙)_𝑠0 are coefficients satisfying the following relationship: 𝐻_𝑠𝑌_𝑘−𝜇_𝑌𝜎_𝑌=^𝑠_𝑗=0𝑑^(𝑙)_𝑠𝑗𝐻_𝑗𝑌_𝑘−𝜉_𝑘𝜎_𝑌. (23) By using the orthonormal condition of Hermite polynomial, ^∞_−∞𝑁𝑌_𝑘;𝜉_𝑘,𝜎²_𝑌1√𝐻𝑖!^𝑖𝑌_𝑘−𝜉_𝑘𝜎_𝑌1𝐻𝑗!^𝑗𝑌_𝑘−𝜉_𝑘𝜎_𝑌𝑑𝑌_𝑘=𝛿_𝑖𝑗, (24) the function 𝐼_𝑙 in (21) can be calculated as 𝐼_𝑙=exp𝐶𝜇_𝑌+𝑙𝜎²_𝑌²2𝜎²_𝑌𝐶²−𝐶𝜇²_𝑌+2𝑙𝜎²_𝑌𝐾2𝜎²_𝑌𝐶×∑^∞_𝑟=0∑^∞_𝑠=0𝐴_𝑟𝑠𝜑_𝑟⁽¹⁾𝑋_𝑘𝑑^(𝑙)_𝑠0/√𝑠!∑^∞_𝑟=0𝐴_𝑟0𝜑_𝑟⁽¹⁾𝑋_𝑘. (25) Therefore, (20) can be expressed as follows: 𝑦_𝑘∣𝑍_𝑘−1=exp𝐶𝜇_𝑌+𝜎²_𝑌²2𝜎²_𝑌𝐶²−𝐶𝜇²_𝑌+2𝜎²_𝑌𝐾2𝜎²_𝑌𝐶×∑^∞_𝑠=0𝐀_(𝑠),𝑘Φ𝑋_𝑘𝑑⁽¹⁾_𝑠0/√𝑠!∑^∞_𝑟=0𝐴_𝑟0𝜑_𝑟⁽¹⁾𝑋_𝑘,𝑦_𝑘−𝑦^∗_𝑘²∣𝑍_𝑘−1=exp𝐶𝜇_𝑌+2𝜎²_𝑌²2𝜎²_𝑌𝐶²−𝐶𝜇²_𝑌+4𝜎²_𝑌𝐾2𝜎²_𝑌𝐶×∑^∞_𝑠=0𝐀_(𝑠),𝑘Φ𝑋_𝑘𝑑⁽²⁾_𝑠0/√𝑠!∑^∞_𝑟=0𝐴_𝑟0𝜑_𝑟⁽¹⁾𝑋_𝑘−𝑦^∗_𝑘, (26) with Φ𝑋_𝑘≡𝜑₀⁽¹⁾𝑋_𝑘,𝜑₁⁽¹⁾𝑋_𝑘,…^𝑡,𝐀_(𝑠),𝑘≡𝐚^∗_(𝑠),𝑘,(),𝐀𝑠=1,2,…_(0),𝑘≡𝐴₀₀,𝐴₁₀,𝐴₂₀,𝐚,…_{∗(𝑠),𝑘}≡𝐚_(𝑠),𝑘∣𝑍_𝑘−1, (27) where 𝑡 denotes the transpose of a matrix. Furthermore, using the definition of Laguerre polynomial and (1), the expansion coefficient 𝐵_𝐦𝑛 can be calculated as follows: 𝐵_𝐦𝑛=Γ𝑚^∗_𝑘𝑛!Γ𝑚^∗_𝑘+𝑛^𝑛_𝑠1=0(−1)^𝑠1𝑛𝑠₁1Γ𝑚𝑛!^∗𝑘+𝑛Γ𝑚^∗_𝑘+𝑠₁1𝑠^∗_𝑘^𝑠1×^𝑠1_𝑠2=0𝑠₁𝑠₂exp𝐶𝜇_𝑌+𝑠₂𝜎²_𝑌²2𝜎²_𝑌𝐶²−𝐶𝜇²_𝑌+2𝑠₂𝜎²_𝑌𝐾2𝜎²_𝑌𝐶×𝑣^{𝑠1−𝑠2}_𝑘×∑^∞_𝑠=0𝜃_𝐦⁽¹⁾𝐚_𝑘𝐚_(𝑠),𝑘∣𝑍_𝑘−1Φ𝑋_𝑘1/√𝑠!𝑑^(𝑠2)_𝑠0∑^∞_𝑟=0𝐴_𝑟0𝜑_𝑟⁽¹⁾𝑋_𝑘. (28) From (19) and (26)–(28), it can be found that the parameters 𝑧^∗_𝑘, Ω_𝑘 and the expansion coefficient 𝐵_𝐦𝑛 are given by the predictions of unknown parameter 𝐚_𝑘, the statistics of the external noise 𝑣_𝑘, and the input observations 𝑋_𝑘.

By considering (11), the prediction to perform the recurrence estimation can be given for an arbitrary polynomial function 𝑔_𝐍(𝐚_𝑘+1) with 𝐍th order of 𝐚_𝑘+1 can be expressed as 𝑔^∗_𝐍𝐚_𝑘+1≡𝑔_𝐍𝐚_𝑘+1∣𝑍_𝑘=𝑔_𝐍𝐚_𝑘∣𝑍_𝑘=𝑔_𝐍𝐚_𝑘. (29)

2.3. Prediction of Output Probability Distribution for Sound Environment System

Because the conditional probability density function 𝑃(𝑌∣𝑋) can be considered as an invariant system characteristic, reflecting mainly the proper correlation relationship between the two sound pressure levels 𝑋 and 𝑌 in the sound environment system, the output probability distribution 𝑃_𝑠(𝑌) in decibel scale can be predicted, as 𝑃_𝑠(𝑌)=⟨𝑃(𝑌∣𝑋)⟩_𝑋. Thus, based on (1) and using the estimated parameter 𝐚_𝑘≡(𝐴₁₁,𝐴₁₂,…), the output probability density function 𝑃_𝑠(𝑌) can be predicted from the observed input data 𝑋, as follows: 𝑃_𝑠(𝑌)=𝑃₀(𝑌)^∞_𝑠=0∑^∞_𝑟=0𝐴_𝑟𝑠𝜑_𝑟⁽¹⁾(𝑋)∑^∞_𝑟=0𝐴_𝑟0𝜑_𝑟⁽¹⁾(𝑋)_𝑋𝜑_𝑠⁽²⁾(𝑌). (30)

3. Application to Real Sound Environment System

3.1. Application to Sound Insulation System

In order to confirm the effectiveness of the proposed method, it was applied to real data observed in a sound environment system. Acoustic signals observed by two microphones in indoors and outdoors for a house were adopted as input and output data for the sound insulation system. The schematic drawing of the sound environment system is shown in Figure 1. The rock music was selected as an input signal by considering the aggravation of “Karaoke” noise pollution problem, and white noise was adopted as a background noise. The statistics of the input, output signals and the background noise are shown in Table 1. The input and output fluctuation data simultaneously measured with every sampling interval 1 s. Based on the 500 data, the expansion coefficients 𝐴_𝑟𝑠 in (1) were estimated on the basis of the input signal 𝑋_𝑘 and observation 𝑧_𝑘 under existence of the background noise.

Table 1 

Statistics of the input, output signals and the background noise in sound insulation system.

Figure 1 

A schematic drawing of the experimental setup in sound insulation system.

Based on the estimated expansion coefficients, the output response probability distribution excited by an arbitrary input signal was predicted. The 200 sampled data following the data used for the evaluation of expansion coefficients were adopted for predicting the output response probability distribution. Figure 2 shows the comparison between theoretically predicted curves and experimentally sampled points on the output probability distribution. The cumulative distributions of sound level are shown in this figure. The “theoretical curves” in this figure were obtained by predicting the probability density function of the output level 𝑌 based on the observed data of the input level 𝑋 by use of the theoretical expression. In this figure, “1st–3rd approximations” considered the expansion coefficients 𝐴₁₁,𝐴₁₂,𝐴₂₁, and 𝐴₂₂ successively in (30). The “experimental values” represent the frequency distributions obtained directly from the observed data of the output level 𝑌.

Figure 2 

Comparison between experimentally sampled values and theoretically predicted curves by the proposed method on the output probability distribution for the sound insulation system.

For comparison, the prediction results of the output probability distribution by introducing the standard regression models described by the following equations are shown in Figure 3: 𝑌=𝑎₁+𝑏₁𝑋(1stordermodel),𝑌=𝑎₂+𝑏₂𝑋+𝑐₂𝑋²(2ndordermodel),𝑌=𝑎₃+𝑏₃𝑋+𝑐₃𝑋²+𝑑₃𝑋³(3rdordermodel). (31) The regression coefficients 𝑎_𝑖,𝑏_𝑖,𝑐_𝑖, and 𝑑_𝑖 in (31) were determined by applying the extended Kalman filter [5] after introducing the following observation equation: 𝑧_𝑘=10^{𝑌𝑘/10−12}+𝑣_𝑘. (32) The theoretically predicted curves based on the proposed method show better agreement with the experimentally sampled values than the results by applying the extended Kalman filter based on the standard regression models.

Figure 3 

Comparison between experimentally sampled values and theoretically predicted curves by the standard regression models on the output probability distribution for the sound insulation system.

From the cumulative distributions in Figures 2 and 3, noise evaluation quantities 𝐿_𝑥 ((100 −  𝑥) percentile level) can be evaluated. Several noise evaluation quantities obtained from these figures are shown in Table 2. It is obvious that the proposed method provides a more accurate prediction than the results based on the standard regression models.

Table 2 

Comparison between the experimental values and theoretically predicted values for several noise evaluation quantities in dB evaluated from Figures 2 and 3.

3.2. Application to Road Traffic Noise Environment

The effectiveness of the proposed method was confirmed experimentally by applying it to real road traffic noise data observed in the complicated sound environment near a national road.

In order to evaluate the sound environment around the main line, the sound level at an evaluation point has to be predicted on the basis of the observation at a reference point. After regarding the sound levels at a reference point and an evaluation point as system input 𝑋 and output 𝑌, respectively, the probability distribution in decibel scale at the evaluation point connected with several evaluation quantities of the sound environment was predicted on the basis of the observation at the reference point. The reference point and the evaluation point were chosen at the positions being 1 m and 25 m apart from one side of the road as shown in Figure 4. The statistics of the input, output signals and the background noise are shown in Table 3. By applying the proposed method, the probability density function of the sound level at the evaluation point was predicted on the basis of the observation at the reference point. Road traffic noise was measured by the use of the sound level meter at every 0.2 s. Through the same procedure in Section 3.1, the expansion coefficients 𝐴_𝑟𝑠 in (1) were first estimated.

Table 3 

Statistics of the input, output signals and the background noise in road traffic noise environment.

Figure 4 

A schematic drawing of the experiment in road traffic noise environment near a national road.

Based on the estimates of the expansion coefficients, the probability density function of road traffic noise at the evaluation point was predicted by measuring the road traffic noise data at the reference point. The predicted results are shown in Figures 5 and 6. Several noise evaluation quantities evaluated from Figures 5 and 6 are shown in Table 4. From these results, it can be seen that the theoretically predicted values by use of the proposed method show good agreement with the experimental values, as compared with the results applying the extended Kalman filter for the standard regression models.

Table 4 

Comparison between the experimental values and theoretically predicted values for several noise evaluation quantities in dB evaluated from Figures 5 and 6.

Figure 5 

Comparison between experimentally sampled values and theoretically predicted curves by the proposed method on the probability distribution at the evaluation point for the road traffic noise environment near a national road.

Figure 6 

Comparison between experimentally sampled values and theoretically predicted curves by the standard regression models on the probability distribution at the evaluation point for the road traffic noise environment near a national road.

4. Conclusions

In this paper, an evaluation method of complex sound environment systems under existence of an external noise has been proposed. More specifically, by paying attention to the energy variables satisfying the additive property of the specific signal and the external noise, a method for estimating the correlation information between the input and output variables has been theoretically derived on the basis of the observations contaminated by the external noise. Furthermore, a prediction method of the output probability distribution in decibel scale has been derived based on the observations of the input level. The proposed prediction method has been realized by introducing a sound environment model of the conditional probability type in decibel scale. The proposed method has then been applied to the estimation and prediction of a real sound insulation system and road traffic noise environment, and it has been experimentally verified that good results have been achieved with this method.

The proposed stochastic signal processing method is quite different from the traditional standard approach. However, it is still at its early stage of study, and there are a number of practical problems to be explored in the future, starting from the result of the basic study in this paper. Some of the problems are the following.(i)The proposed method should be applied to real prediction problems of output probability distribution for many other sound environment systems, and its practical usefulness should be verified in each real situation.(ii)The theory should be extended to further practical cases with multi-input and multioutput systems.(iii)An optimal number of expansion terms in the proposed stochastic signal processing method of expansion expression type should be found.

Appendix

Derivation of the Estimate

The conditional joint probability density function of the parameter 𝐚_𝑘 and the observation 𝑧_𝑘 can be generally expanded in a statistical orthogonal expansion series: 𝑃𝐚_𝑘,𝑧_𝑘∣𝑍_𝑘−1=𝑃₀𝐚_𝑘∣𝑍_𝑘−1𝑃₀𝑧_𝑘∣𝑍_𝑘−1×^∞∞_{𝐦=𝟎𝑛=0}𝐵_𝐦𝑛𝜃_𝐦⁽¹⁾𝐚_𝑘𝜃_𝑛⁽²⁾𝑧_𝑘. (A.1) After substituting (A.1) into (12), taking the conditional expectation of the function 𝑓_𝐌(𝐚_𝑘), and using the orthonormal condition for the function 𝜃_𝐦⁽¹⁾(𝐚_𝑘), (13) can be derived.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.