Archives of Acoustics, 46, 1, pp. 55–65, 2021
10.24425/aoa.2021.136560

Development of a Sound Quality Evaluation Model Based on an Optimal Analytic Wavelet Transform and an Artificial Neural Network

Mehdi POURSEIEDREZAEI
Isfahan University of Technology
Iran, Islamic Republic of

Ali LOGHMANI
Isfahan University of Technology
Iran, Islamic Republic of

Mehdi KESHMIRI
Isfahan University of Technology
Iran, Islamic Republic of

The purpose of this study was to develop a sound quality model for real time active sound quality control systems. The model is based on an optimal analytic wavelet transform (OAWT) used along with a back propagation neural network (BPNN) in which the initial weights and thresholds are determined by particle swarm optimisation (PSO). In the model the input signal is decomposed into 24 critical bands to extract a feature matrix, based on energy, mean, and standard deviation indices of the sub signal scalogram obtained by OAWT. The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. The results of the study show that the present model is in good agreement with psychoacoustic models of sound quality metrics and enables evaluation of the quality of sound at a lower computational cost than the existing models.
Keywords: analytic wavelet transform (AWT), sound quality evaluation (SQE), psychoacoustic metrics, back propagation neural network (BPNN)
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References

Aures W. (1985), Method for calculating auditory roughness [in German: Ein Berechnungsverfahren der Rauhigkeit], Acta Acustica united with Acustica, 58(5): 268–281.

Aures W. (1985), Calculation method for the sensory euphony of any sound signals [in German: Berechnungsverfahren für den sensorischen Wohlklang beliebiger Schallsignale, Acta Acustica united with Acustica, 59(2): 130–141.

Blauert J., Jekosch U. (1998), Product-sound quality: A new aspect of machinery noise, Archives of Acoustics, 23(1): 105–124.

Chen K., Paurobally R., Pan J., Qiu X. (2015), Improving active control of fan noise with automatic spectral reshaping for reference signal, Applied Acoustics, 87, 142–152, doi: 10.1016/j.apacoust.2014.07.003.

Cuddy L.L., Russo F.A., Galembo A. (2007), Tonality of Low-Frequency Synthesized Piano Tones, Archives of Acoustics, 32(4): 541–550.

Dunn M.S., Erickson D., Avenue H., Gregory S. (2013), Recommended standards for newborn ICU design, eighth edition, Journal of Perinatology, 33(Suppl. 1): S2–S16, doi: 10.1038/jp.2013.10.

Fastl H., Zwicker E. (2007), Psychoacoustics: facts and models, Springer, Berlin, Germany, 3rd ed., doi: 10.1007/978-3-540-68888-4.

Fausett L. (1994), Fundamentals of Neural Networks, Englewood Cliffs, NJ: Prentice-Hall.

Hafke-Dys H., Preis A., Kaczmarek T., Biniakowski A., Kleka P. (2016), Noise annoyance caused by amplitude modulated sounds resembling the main characteristics of temporal wind turbine noise, Archives of Acoustics, 41(2): 221–232, doi: 10.1515/aoa-2016-0022.

Hecht-Nielsen R. (1992), Theory of the Backpropagation Neural Network, [in:] Neural networks for perception, Vol. 2: Computation, Learning, Architectures, H. Wechsler, G.M. Univ., V. Fairfax (Eds) Harcourt Brace & Co., Orlando, FL, USA, doi: 10.5555/140639.140643.

Huang H.B., Li R.X., Huang X.R., Yang M.L., Ding W.P. (2015), Sound quality evaluation of vehicle suspension shock absorber rattling noise based on the Wigner-Ville distribution, Applied Acoustics, 100: 18–25, doi: 10.1016/j.apacoust.2015.06.018.

Huang H.B., Li R.X., Yang M.L., Lim T.C., Ding W.P. (2017), Evaluation of vehicle interior sound quality using a continuous restricted Boltzmann machine-based DBN, Mechanical Systems and Signal Processing, 84(Part A): 245–267, doi: 10.1016/j.ymssp.2016.07.014.

Jaddi N.S., Abdullah S. (2018), Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting, Engineering Applications of Artificial Intelligence, 67, 246–259, doi: 10.1016/j.engappai.2017.09.012.

Kaczmarek T., Preis A. (2010), Annoyance of time-varying road-traffic noise, Archives of Acoustics, 35(3): 383–393, doi: 10.2478/v10168-010-0032-2.

Klonari D., Pastiadis K., Papadelis G., Papanikolao G. (2011), Loudness assessment of musical tones equalized in a-weighted level, Archives of Acoustics, 36(2): 239–250, doi: 10.2478/v10168-011-0019-7.

Kuo S.M., Morgan D. (1996), Active Noise Control Systems: Algorithms and DSP Implementations, John Wiley & Sons, Inc., New York, NY, USA.

Lee H.H., Lee S. K. (2009), Objective evaluation of interior noise booming in a passenger car based on sound metrics and artificial neural networks, Applied Ergonomics, 40(5): 860–869, doi: 10.1016/j.apergo.2008.11.006.

Leite R.P., Paul S., Gerges S.N.Y. (2008), A sound quality-based investigation of the HVAC system noise of an automobile model, Applied Acoustics, 70: 1–10, doi: 10.1016/j.apacoust.2008.06.010.

Lyon R. (2000), Designing for product sound quality, Mechanical Engineering, Marcel Dekker, Inc.: New York, Basel.

Maleczek S. (2008), Testing the sound quality of acoustic vacuum tube amplifier, Archives of Acoustics, 33(4 suppl.), 135–140.

Miśkiewicz A., Rogala T., Szczepańska-Antosik J. (2007), Perceived roughness of two simultaneous harmonic complex tones, Archives of Acoustics, 32(3): 737–748.

NI-Tutorial-1526 (2013), White paper on Measurement of Sound Quality.

Olbrych S. (2010), Noise pollution in the NICU, Case West. Reserv. Univ.

Parsons C.E., Young K.S., Craske M.G., Stein A.L., Kringelbach M.L. (2014), Introducing the oxford vocal (OxVoc) sounds database: A validated set of non-acted affective sounds from human infants, adults, and domestic animals, Frontiers in Psychology, 5: 562, doi: 10.3389/fpsyg.2014.00562.

Pleban D. (2014), Definition and measure of the sound quality of the machine, Archives of Acoustics, 39(1): 17–23, doi:10.2478/aoa-2014-0003.

Pourseiedrezaei M., Loghmani A., Keshmiri M. (2019), Prediction of psychoacoustic metrics using combination of wavelet packet transform and an optimized artificial neural network, Archives of Acoustics, 44(3): 561–573, doi: 10.24425/aoa.2019.129271.

Szczepańska-Antosik J. (2008), Roughness of two simultaneous harmonic complex tones in various pitch registers, Archives of Acoustics, 33(1): 73–78.

Technical note (2015, October), An introduction to sound quality testing, Manchester, UK: Acoustic Research Centre, School of Computing, Science and Engineering, University of Salford.

Terhardt E., Stoll G., Seewann M. (1982), Algorithm for extraction of pitch and pitch salience from complex tonal signals, The Journal of the Acoustical Society of America, 71(3): 679–688, doi: 10.1121/1.387544.

Vencovský V. (2016), Roughness prediction based on a model of cochlear hydrodynamics, Archives of Acoustics, 41, 189–201, doi: 10.1515/aoa-2016-0019.

Wang Y.S., Lee C.M., Kim D.G., Xu Y. (2007), Sound-quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model, Journal of Sound and Vibration, 299, 933–947, doi: 10.1016/j.jsv.2006.07.034.

Wang Y.S., Shen G.Q., Xing Y. F. (2014), A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network, Mechanical Systems and Signal Processing, 45, 255–266, doi: 10.1016/j.ymssp.2013.11.001

Xing Y.F.F., Wang Y.S.S., Shi L., Guo H., Chen H. (2016), Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods, Mechanical Systems and Signal Processing, 66–67: 875–892, doi: 10.1016/j.ymssp.2015.05.003

Zhang J.R., Zhang J., Lok T.M., Lyu M.R. (2007), A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training, Applied Mathematics and Computation, 185(2): 1026–1037, doi: 10.1016/j.amc.2006.07.025.

Zhu X., Kim J. (2006), Application of analytic wavelet transform to analysis of highly impulsive noises, Journal of Sound and Vibration, 294(4): 841–855, doi: 10.1016/j.jsv.2005.12.034.




DOI: 10.24425/aoa.2021.136560

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