Acoustic Design in Urban Development : analysis of urban soundscapes and acoustic ecology research in New York City

The world is urbanizing rapidly with more than half of the global population now living in cities. Improving urban environments for the well-being of the increasing number of urban citizens is becoming one of the most important challenges of the 21st century. Even though it is common that city planners have visions of a ’good urban milieu’, those visions are concerning visual aesthetics or practical matters. The qualitative perspective of sound, such as sonic diversity and acoustic ecology are neglected aspects in architectural design. Urban planners and politicians are therefore largely unaware of the importance of sounds for the intrinsic quality of a place. Whenever environmental acoustics is on the agenda, the topic is noise abatement or noise legislation – a quantitative attenuation of sounds. Some architects may involve acoustical aspects in their work but sound design or acoustic design has yet to develop to a distinct discipline and be incorporated in urban planning.My aim was to investigate to what extent the urban soundscape is likely to improve if modern architectural techniques merge with principles of acoustics. This is an important, yet unexplored, research area. My study explores and analyses the acoustical aspects in urban development and includes interviews with practitioners in the field of urban acoustics, situated in New York City. My conclusion is that to achieve a better understanding of the human living conditions in mega-cities, there is a need to include sonic components into the holistic sense of urban development.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Acoustic Variability in the Production of English Vowels by Native and Non-Native Speakers

The features of non-native speech which distinguish it from native speech are often difficult to pin down. It is possible to be a native speaker of any of a vast number of varieties of English. These varieties each have their phonetic characteristics which allow them to be identified by speakers of the varieties in question and by others. The phonetic differences between the accents represented by these varieties are very great. It is impossible to indicate any particular configuration of vowels in the acoustic vowel space or set of consonant articulations which all native-speaker varieties of English have in common and which non-native speakers do not share. This study considers the vowel quality in a single word by native and non-native speakers.

Children's recall of words spoken in their first and second language : Effects of Signal-to-Noise Ratio and Reverberation Time

Speech perception runs smoothly and automatically when there is silence in the background, but when the speech signal is degraded by background noise or by reverberation, effortful cognitive processing is needed to compensate for the signal distortion. Previous research has typically investigated the effects of signal-to-noise ratio (SNR) and reverberation time in isolation, whilst few have looked at their interaction. In this study, we probed how reverberation time and SNR influence recall of words presented in participants' first- (L1) and second-language (L2). A total of 72 children (10 years old) participated in this study. The to-be-recalled wordlists were played back with two different reverberation times (0.3 and 1.2 s) crossed with two different SNRs (+3 dBA and +12 dBA). Children recalled fewer words when the spoken words were presented in L2 in comparison with recall of spoken words presented in L1. Words that were presented with a high SNR (+12 dBA) improved recall compared to a low SNR (+3 dBA). Reverberation time interacted with SNR to the effect that at +12 dB the shorter reverberation time improved recall, but at +3 dB it impaired recall. The effects of the physical sound variables (SNR and reverberation time) did not interact with language. © 2016 Hurtig, Keus van de Poll, Pekkola, Hygge, Ljung and Sörqvist.