Architectural acoustics illustrated

Ermann, Michael

doi:10.1121/1.4920562

Cited by 27 publications

(19 citation statements)

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“…It can be perceived as prolonging of the original sound due to temporal smearing and in most real-life cases as an audible change in timbre. In the case of music, reverberation is to some extent desirable since it provides warmth to the sound [1]. In the case of speech, it can degrade intelligibility [2] and also affects the performance of Automatic Speech Recognition (ASR) systems [3].…”

Section: Introductionmentioning

confidence: 99%

Discriminative feature domains for reverberant acoustic environments

Papayiannis

Evers

Naylor

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Several speech processing and audio data-mining applications rely on a description of the acoustic environment as a feature vector for classification. The discriminative properties of the feature domain play a crucial role in the effectiveness of these methods. In this work, we consider three environment identification tasks and the task of acoustic model selection for speech recognition. A set of acoustic parameters and Machine Learning algorithms for feature selection are used and an analysis is performed on the resulting feature domains for each task. In our experiments, a classification accuracy of 100% is achieved for the majority of tasks and the Word Error Rate is reduced by 20.73 percentage points for Automatic Speech Recognition when using the resulting domains. Experimental results indicate a significant dissimilarity in the parameter choices for the composition of the domains, which highlights the importance of the feature selection process for individual applications.

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Section: Introductionmentioning

confidence: 99%

Discriminative feature domains for reverberant acoustic environments

Papayiannis

Evers

Naylor

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…As frequências audíveis são divididas em três regiões, entre 20 e 200 Hz é possível escutar sons graves, entre 200 e 2.000 Hz é possível ouvir sons médios, e entre 2.000 e 20.000 Hz encontram-se os sons agudos (BIES; HANSEN; HOWARD, 2018). Os três fenômenos físicos que caracterizam o som são: (1) Nível sonoro (ou energia, força, amplitude, intensidade sonora); (2) Frequência (ou tom comprimento de onda); e (3) Propagação (ou caminho, tempo decorrido) (ERMAN, 2015).…”

Section: Desempenho Acústicounclassified

Avaliação em campo do ruído aéreo de uma sala de aula tipo em campus universitário

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Postay

Petereit

et al. 2019

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O pensamento a respeito do desempenho acústico nas edificações é consideravelmente novo no âmbito da construção civil especialmente no Brasil, onde, os requisitos de desempenho normalizados se aplicam apenas para edificações habitacionais através do conjunto de normas da ABNT NBR 15575:2013, também conhecida como norma de desempenho, enquanto que em diversos países há uma exigência mínima especifica para outras tipologias. Neste artigo, são apresentados alguns estudos de caso em edificações escolares, onde são demonstradas pesquisas realizadas tanto com medições em campo como avaliações qualitativas. Vale destacar que o processo de ensino aprendizagem necessita da escuta e compreensão da fala em todo o ambiente, possibilitando a boa interlocução entre professor e alunos. O objetivo deste artigo é avaliar o isolamento ao ruído aéreo de uma sala de aula tipo de uma instituição de ensino de nível superior. Tendo como objeto de estudo uma sala de aula de campus universitário localizado no estado do RS que possui 114,24 m2. A investigação da pesquisa foi através de verificação em campo do isolamento ao ruído aéreo, com ensaios conforme procedimentos da ISO 16283-1 para os ensaios de sistemas de vedações verticais internos e da ISO 16283-3 para os ensaios nos sistemas externos (fachadas). Os resultados apresentam desempenho abaixo do previsto quando comparados a referências internacionais específicas para ambiente escolar e, ainda, se considerados os requisitos estabelecidos na NBR 15575-4 o sistema construtivo também não atenderia o nível mínimo de desempenho.

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“…All values in the space are considered as candidate TOAs of reflections and the only remaining unknowns are their amplitudes w. The unknown number of reflections is estimated asD = w 0 , where · 0 counts the number of non-zero elements of the vector. These elements correspond to the scaling coefficients β i in (1). For the early part of the AIR, w is expected to be sparse as the reflection density is low.…”

Section: B Initial Parameter Estimationmentioning

confidence: 99%

“…It offers warmth to the sound and it is desirable in the case of music [1]. In the case of speech, it can degrade the performance of Automatic Speech Recognition (ASR) systems and impact intelligibility [2].…”

Section: Introductionmentioning

confidence: 99%

Sparse parametric modeling of the early part of acoustic impulse responses

Papayiannis

Evers

Naylor

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Acoustic channels are typically described by their Acoustic Impulse Response (AIR) as a Moving Average (MA) process. Such AIRs are often considered in terms of their early and late parts, describing discrete reflections and the diffuse reverberation tail respectively. We propose an approach for constructing a sparse parametric model for the early part. The model aims at reducing the number of parameters needed to represent it and subsequently reconstruct from the representation the MA coefficients that describe it. It consists of a representation of the reflections arriving at the receiver as delayed copies of an excitation signal. The Time-Of-Arrivals of reflections are not restricted to integer sample instances and a dynamically estimated model for the excitation sound is used. We also present a corresponding parameter estimation method, which is based on regularized-regression and nonlinear optimization. The proposed method also serves as an analysis tool, since estimated parameters can be used for the estimation of room geometry, the mixing time and other channel properties. Experiments involving simulated and measured AIRs are presented, in which the AIR coefficient reconstruction-error energy does not exceed 11.4% of the energy of the original AIR coefficients. The results also indicate dimensionality reduction figures exceeding 90% when compared to a MA process representation.

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Architectural acoustics illustrated

Cited by 27 publications

References 0 publications

Discriminative feature domains for reverberant acoustic environments

Discriminative feature domains for reverberant acoustic environments

Avaliação em campo do ruído aéreo de uma sala de aula tipo em campus universitário

Sparse parametric modeling of the early part of acoustic impulse responses

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