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- Técnica híbrida segmentada ponderada aplicada à estimação de parâmetros acústicos de salas (Hibrid Segmented Swept Sine)Publication . Preto Paulo, Joel; Martins, Carlos Rodrigues; Coelho, J. L. BentoA aplicação da técnica de Varrimento Sinusoidal Logarítmico - Swept Sine - aplicada à acústica de salas tornou-se usual devido à sua elevada SNR e robustez contra pequenos valores de não-linearidades e variância no tempo da sala em teste. No entanto, a presença de fontes de ruído não estacionário, por exemplo presença de pessoas na vizinhança, ruído de génese viária ou ainda ruído proveniente do decorrer das actividades de espectáculo pode implicar um valor de SNR baixo para uma correcta estimação dos parâmetros acústicos. Este tipo de ruído apresenta grandes flutuações de energia ao longo do tempo e da frequência. Este estudo explora esta característica no sentido de permitir um aumento da SNR no final do processamento. A técnica Hibrid Segmented Swept Sine consiste em enviar para a sala com um conjunto de M tramas Swept Sine. Cada trama é dividida em N segmentos e onde cada segmento é analisado por bandas de frequência através da aplicação de um banco de filtros, ou seja, é feita uma análise fina da energia nos domínios do tempo e da frequência. A partir das energias obtém-se uma função de ponderação no tempo e na frequência que é aplicada às tramas utilizando de seguida a técnica de médias temporais. Testes realizados para vários tipos de sinais de ruído revelaram incrementos significativos da SNR. A técnica Híbrida de Segmentação Ponderada mostrou que, para determinados tipos de sinal de ruído, existem incrementos de SNR superiores a 15 dB (ruído do tipo fala) em relação à técnica Swept Sine.
- A hybrid MLS technique for room impulse response estimationPublication . Preto Paulo, Joel; Martins, Carlos Rodrigues; Bento Coelho, J. L.The measurement of room impulse response (RIR) when there are high background noise levels frequently means one must deal with very low signal-to-noise ratios (SNR). if such is the case, the measurement might yield unreliable results, even when synchronous averaging techniques are used. Furthermore, if there are non-linearities in the apparatus or system time variances, the final SNR can be severely degraded. The test signals used in RIR measurement are often disturbed by non-stationary ambient noise components. A novel approach based on the energy analysis of ambient noise - both in the time and in frequency - was considered. A modified maximum length sequence (MLS) measurement technique. referred to herein as the hybrid MLS technique, was developed for use in room acoustics. The technique consists of reducing the noise energy of the captured sequences before applying the averaging technique in order to improve the overall SNRs and frequency response accuracy. Experiments were conducted under real conditions with different types of underlying ambient noises. Results are shown and discussed. Advantages and disadvantages of the hybrid MLS technique over standard MLS technique are evaluated and discussed. Our findings show that the new technique leads to a significant increase in the overall SNR. (C) 2008 Elsevier Ltd. All rights reserved.
- Identification of road pavement types using bayesian analysis and neural networksPublication . Preto Paulo, Joel; Coelho, J. Louis BentoA new method to classify and identify different types of road pavements by analysing the near field sound profile and texture using statistical learning methods is proposed. A set of characteristics were extracted from the noise profile and from the road surface texture. Sound measurements were carried out following the close-proximity method with the texture descriptors being provided by a high speed profilometer system. As a first approach, it is assumed that the features extracted from the noise and texture characteristics follow normal distributions. However, this assumption is not completely verified for all types of road surfaces. The method presented herein exploits the use of Bayesian analysis complemented by a neural network in order to improve the classification results.
- Statistical classification of road pavements using near field vehicle rolling noise measurementsPublication . Preto Paulo, Joel; Coelho, J.; Figueiredo, MárioLow noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.