Purpose - Despite that different methods for the inclusion of transport noise in life cycle assessment (LCA) have been proposed, none of them has become consensual. Leveraging a case study on car tires, this paper aims at comparing two among these characterization approaches to identify strengths and weaknesses and to investigate the relative contribution of noise to human health (in disability-adjusted life years (DALYs)) as compared to other environmental stressors. Methods - The case study analyzed two tires showing different acoustical properties. The two methods applied are the one developed by Müller-Wenk and further improved by other authors and the recent one proposed by Cucurachi. These two methods were adapted to the case study, and a full LCA study of the car tires was carried out. Both uncertainty and sensitivity analyses were performed. Results and discussion - Both methods highlight the potential high contribution of noise damage to the DALYs generated by car tires, even considering parameters’ uncertainties. This study shows therefore the necessity to integrate noise impact in LCA in a broader way. Both methods present coherent results regarding the environmental performance differences between the two products. However, the absolute DALY scores differ by eight orders of magnitude, probably because Cucurachi’s methods overestimate the damages. The analysis of modeling choices and parameter uncertainties could not explain this difference. Conclusions - Noise impact on human health has to be included in LCA, and additional efforts should focus on the characterization modeling since there is not yet a consensual method for a systematic integration. The case study shows that the improvement of tire design can efficiently reduce noise impact on human health. Both methods have advantages and inconveniences. We think that it is possible to elaborate a method combining the strengths of both approaches. An incremental approach used on accurate localized and temporalized data processed with noise propagation software could provide characterization factors for a set of archetypes. This should be a good compromise for a method allowing systematic integration of noise impact in LCA.