Design and Evaluation of Electric Vehicle Sound Using Granular Synthesis

Lazaro, May Jorella; Kim, Sungho; Choi, Minsik; Kim, Ki-Chang; Park, Dongchul; Moon, Soyoun; Yun, Myung Hwan

doi:10.17743/jaes.2021.0062

Cited by 3 publications

(3 citation statements)

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“…This is a commonly used method for the assessment of sounds Otto et al, 2001). In accordance with the findings of the previous studies (Bisping 1997;Lazaro et al, 2022;Qi & Yang 2021;Västfjäll et al, 2002;Yamauchi et al, 2004), the latent variables were measured through the following adjective pairs: pragmatic quality (vague-clear, unaware-aware, not refinedrefined), hedonic quality (unpleasant-pleasant, uncomfortablecomfortable, weak-power), novelty (common-unique, familiarunfamiliar, traditional-futuristic), user preference (unsatisfiedsatisfied, dislike-like, not suitable-suitable).…”

Section: Methodssupporting

confidence: 86%

“…Twenty-seven EV acceleration sound samples, originally adopted from the study of Lazaro et al (2022), which was generated through granular synthesis with varying psychoacoustic qualities were used as the stimuli for the experiment. The sound samples used were designed to mimic the sound of a vehicle accelerating in a semi-enclosed space (e.g.…”

Section: Stimulimentioning

confidence: 99%

“…tunnel). Each sound sample lasted for approximately 10 seconds (for more details about the sound samples and how it was generated, refer to the study of Lazaro et al, 2022). The sound samples were presented in an acoustically well-damped room over AKG-K601 headphones and Styleaudio CARAT-RUBY2 sound amplifiers to ensure sound quality.…”

Section: Stimulimentioning

confidence: 99%

See 2 more Smart Citations

Factors Affecting Interior Acceleration Sound Preference for Electric Vehicles: A Path Analysis

Lazaro

Kim

Anggang

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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Vehicle sound design is gaining attention in the automotive industry, especially for Electric Vehicles (EV). For EVs, acceleration sound is critical for both user experience (UX) and safety. Despite the abundance of UX-related studies investigating the external presentation of acceleration sound for EVs, internal presentation of acceleration sound seems to be overlooked. Thus, further understanding on what influences the user preferences for internal EV sound is essential for better EV sound design. This study aims to explore and develop a simple theoretical path model to help understand the relationship between pragmatic quality, hedonic quality, novelty, and user preferences for EV internal acceleration sounds. Thirty-two participants evaluated twenty-seven EV acceleration sound samples using a 12-item semantic differential scale with bipolar adjective pairs that describe the measured variables in a controlled experimental setting. The relationship between the modeled variables was analyzed using bias-corrected factor score path analysis (BCFSPA). Results showed that the modified model yielded good model fit indices and partially confirmed the initial hypotheses. It was found that pragmatic and hedonic quality had a positive relationship with user preference, whereas, novelty had a negative relationship with hedonic quality and user preference for EV sounds. This study contributes to the understanding of factors that affects user preference for EV sound and provides initial accounts to different approaches and methods for model testing.

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Section: Methodssupporting

confidence: 86%

Section: Stimulimentioning

confidence: 99%

See 1 more Smart Citation

Factors Affecting Interior Acceleration Sound Preference for Electric Vehicles: A Path Analysis

Lazaro

Kim

Anggang

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

Self Cite

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Real-Time Automotive Engine Sound Simulation with Deep Neural Network

Li,

Wang,

2024

Communications in Computer and Information Science

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Research on Design of Electric Vehicle Sound Synthesis Based on Frequency Shift Algorithm

Yu,

Xie,

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">The active sound generation systems (ASGS) for electric vehicles (EVs) play an important role in improving sound perception and transmission in the car, and can meet the needs of different user groups for driving and riding experiences. The active sound synthesis algorithm is the core part of ASGS. This paper uses an efficient variable-range fast linear interpolation method to design a frequency-shifted and pitch-modified sound synthesis algorithm. By obtaining the operating parameters of EVs, such as vehicle speed, motor speed, pedal opening, etc., the original sound signal is interpolated to varying degrees to change the frequency of the sound signal, and then the amplitude of the sound signal is determined according to different driving states. This simulates an effect similar to the sound of a traditional car engine. Then, a dynamic superposition strategy is proposed based on the Hann window function. Through windowing and superposition processing of each sound signal segment generated by the algorithm, the coherence and real-time performance of the synthesized engine sound are improved, so that the ASGS can quickly and accurately reflect the driving status of EVs. Finally, through the analysis and verification of the sound quality of the synthesized sound through different parameter adjustments, an engine synthesized sound that satisfies the subjective evaluation of sound quality can be obtained. This paper proposes an effective active sound synthesis algorithm for EVs, which ensures that EVs produce more textured engine sound while emphasizing the timeliness of synthesized sound. It plays an important role in improving pedestrian safety perception and driving experience, and promotes the research and development of ASGS for EVs.</div></div>

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Design and Evaluation of Electric Vehicle Sound Using Granular Synthesis

Cited by 3 publications

References 0 publications

Factors Affecting Interior Acceleration Sound Preference for Electric Vehicles: A Path Analysis

Factors Affecting Interior Acceleration Sound Preference for Electric Vehicles: A Path Analysis

Real-Time Automotive Engine Sound Simulation with Deep Neural Network

Research on Design of Electric Vehicle Sound Synthesis Based on Frequency Shift Algorithm

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