Vehicle Interior Noise Control and Sound Design Technologies

Editor-in-Chief

Prof. Jun Yang

Institute of Acoustics, Chinese Academy of Sciences, China

ISSN

1541 - 0161 (Print)

2693 - 1443 (Online)

Publication Frequency

Continuously

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

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Volume Arrangement

Vol 58 (2024)

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Featured Articles

Soundscapes in Arab Cities: A Systematic Review and Research Agenda

In the context of Arab cities, this study explores the intricate interplay between cultural, historical, and environmental elements that shape their unique soundscapes. The paper aims to shed light on this underrepresented field of study by employing a three-fold research approach: systematic review, a comprehensive literature review, and the formulation of a future research agenda. The first part of the investigation focuses on research productivity in the Arab world regarding soundscape studies. An analysis of publication trends reveals that soundscape research in Arab cities is still an emerging area of interest. Critical gaps in the existing body of literature are identified, highlighting the importance of addressing these gaps within the broader context of global soundscape research. The second part of the study delves into the distinctive features that inform the soundscapes of Arab cities. These features are categorized into three overarching groups: (i) cultural and religious life, (ii) daily life, and (iii) heritage and history, by exploring these factors, the study aims to elucidate the multifaceted nature of Arab urban soundscapes. From the resonating calls to prayer and the vibrant ambiance of traditional cafes to the bustling markets and architectural characteristics, each factor contributes to the auditory tapestry that defines Arab cities. The paper concludes with a forward-looking research agenda, proposing sixteen key questions organized into descriptive and comparative categories. These questions emphasize the need for a more profound understanding of sound perception, sources, and the impact of urban morphology on the soundscape. Additionally, they highlight the need for interdisciplinary research, involving fields such as urban planning, architecture, psychology, sociology, and cultural studies to unravel the complexity of Arab urban soundscapes.

Prediction of Sound Transmission Loss of Vehicle Floor System Based on 1D-Convolutional Neural Networks

The Noise, Vibration, and Harshness (NVH) experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package. As such, accurate and efficient predictions of its sound insulation performance are crucial for optimizing related noise reduction designs. However, the complex acoustic transmission mechanisms and difficulties in characterizing the sound absorption and insulation properties of the floor acoustic package pose significant challenges to traditional Computer-Aided Engineering (CAE) methods, leading to low modeling efficiency and prediction accuracy. To address these limitations, a hierarchical multi-objective decomposition system for predicting the sound insulation performance of the floor acoustic package has been developed based on an analysis of the noise transmission path. This approach involves introducing a 1D-Convolutional Neural Network (1D-CNN) model for predicting the sound insulation performance of the floor acoustic package, thereby avoiding the limitations of conventional CAE approaches that rely solely on “data-driven” methods. The proposed method was applied and tested using specific vehicle models, and the results demonstrated the effectiveness and superiority of the proposed approach relative to those obtained using 2D-CNN and Support Vector Regression (SVR) models.

A Sound Quality Evaluation Method for Vehicle Interior Noise Based on Auditory Loudness Model

When designing and optimizing the hull of vehicles, their sound quality needs to be considered, which greatly depends on the psychoacoustic parameters. However, the traditional psychoacoustic calculation method does not consider the influence of the real human ear anatomic structure, even the loudness which is most related to the auditory periphery. In order to introduce the real physiological structure of the human ear into the evaluation of vehicle sound quality, this paper first carried out the vehicle internal noise test to obtain the experimental samples. Then, the physiological loudness was predicted based on an established human ear physiological model, and the noise evaluation vector was constructed by combining the remaining four psychoacoustic parameters. Finally, the evaluation vector was fitted into the subjective evaluation results of vehicle interior noise by a deep neural network. The results show that our proposed method can estimate the human subjective perception of vehicle interior noise well.

Prediction and Analysis of Vehicle Interior Road Noise Based on Mechanism and Data Series Modeling

Currently, the inexorable trend toward the electrification of automobiles has heightened the prominence of road noise within overall vehicle noise. Consequently, an in-depth investigation into automobile road noise holds substantial practical importance. Previous research endeavors have predominantly centered on the formulation of mechanism models and data-driven models. While mechanism models offer robust controllability, their application encounters challenges in intricate analyses of vehicle body acoustic-vibration coupling, and the effective utilization of accumulated data remains elusive. In contrast, data-driven models exhibit efficient modeling capabilities and can assimilate conceptual vehicle knowledge, but they impose stringent requirements on both data quality and quantity. In response to these considerations, this paper introduces an innovative approach for predicting vehicle road noise by integrating mechanism-driven and data-driven methodologies. Specifically, a series model is devised, amalgamating mechanism analysis with data-driven techniques to predict vehicle interior noise. The simulation results from dynamic models serve as inputs to the data-driven model, ultimately generating outputs through the utilization of the Long Short-Term Memory with Autoencoder (AE-LSTM) architecture. The study subsequently undertakes a comparative analysis between different dynamic models and data-driven models, thereby validating the efficacy of the proposed series vehicle road noise prediction model. This series model, encapsulating the rigid-flexible coupling dynamic model and AE-LSTM series model, not only demonstrates heightened computational efficiency but also attains superior prediction accuracy.