Exhaust systems including mufflers are commonly mounted on engines to reduce the firing cycle noise originating from the combustion process. However, mufflers also produce flow-induced self-noise, originating from the complex flow path throughout the muffler. As an engine prototype is not available in the early stages of a development program, it is challenging to assess the acoustic performance of the full system when only experiment is available. It is also difficult to pinpoint the design features of a muffler generating noise, as a portion of the noise is generated internally.Numerical approaches are a possible alternative. However, capturing non-linear dissipation mechanisms and thermal fluctuations of exhaust flows is challenging, while necessary to accurately predict flow noise. Transient and compressible Computational Fluid Dynamics and Computational AeroAcoustics (CFD/CAA) Lattice-Boltzmann based Methods (LBM) have previously been successfully applied to quantify the flow noise generated by mufflers mounted on an experimental cold flow test bench.In this paper, the accuracy of the method for self-noise predictions of a muffler mounted on a single cylinder engine is demonstrated comparing results with experimental noise measurements obtained in a hemi-anechoic room. The inlet boundary condition of the muffler is calculated using a 1-D system modeling tool, providing oscillating transient temperature and mass flow rate. Measured temperature on the surface of the muffler is prescribed in the simulation on the geometry walls. After validation of the results, the simulation data is further post-processed to identify flow-induced noise sources in the system and propose design changes to reduce self-noise. This study confirms that this numerical approach can be used in a production process to quantify and reduce flow noise in mufflers.