Extending sharpness calculation for an alternative loudness metric input

Evaluation and Improvement of the Sound Quality of a Diesel Engine Based on Tests and Simulations

Traditional acoustic evaluation of a diesel engine generally uses the A-weighted sound pressure level (AWSPL) and radiated sound power to assess the noise of an engine prototype present in an experiment. However, this cannot accurately and comprehensively reflect the auditory senses of human subjects during the simulation stage. To overcome such shortage, the Moore–Glasberg loudness and sharpness approach is applied to evaluate and improve the sound quality (SQ) of a 16 V-type marine diesel engine, and synthesizing noise audio files. Through finite element (FE) simulations, the modes of the engine’s block and the average vibrational velocity of the entire engine surface were calculated and compared with the test results. By further applying an automatically matched layer (AML) approach, the engine-radiated sound pressure level (SPL) and sound power contributions of all engine parts were obtained. By analyzing the Moore–Glasberg loudness and sharpness characteristics of three critical sound field points, an improvement strategy of the oil sump was then proposed. After improvement, both the loudness and sharpness decreased significantly. To verify the objective SQ evaluation results, ten noise audio clips of the diesel engine were then synthesized and tested. The subjective evaluation results were in accordance with the simulated analysis. Therefore, the proposed approach to analyze and improve the SQ of a diesel engine is reliable and effective.

Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications

Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharpness. A chunk of audio is recorded in each node periodically with a microphone array and the binaural rendering is conducted by exploiting the estimated directional characteristics of the incoming sound by means of DOA estimation. Each node computes the parameters in a different location and sends the values to a cloud-based broker structure that allows spatial statistical analysis through Kriging techniques. A cross-validation analysis is also performed to confirm the usefulness of the proposed system.