Additive Manufacturing (AM) processes allow the creation of complex parts with near net shapes. Wire and arc additive manufacturing (WAAM) is an AM process that can produce large metallic components with low material waste and high production rates. Typically, WAAM enables over 10-times the volumetric deposition rates of powder-based AM processes. However, the high depositions rates of WAAM require high heat input to melt the large volume of material, which in turn results in potential flaws such as pores, cracks, distortion, loss of mechanical properties and low dimensional accuracy. Hence, for practical implementation of the WAAM process in an industrial environment it is necessary to ensure flaw-free production. Accordingly, to guarantee the production-level scalability of WAAM it is fundamental to monitor and detect flaw formation during the process. The objective of this work is to characterize the effects of different contaminations on the acoustic spectrum of WAAM and lay the foundations for a microphone-based acoustic sensing approach for monitoring the quality of WAAM-fabricated parts. To realize this objective, WAAM parts were processed with deliberately introduced flaws, such as material contamination, and the acoustic signals were analyzed using the time and frequency domain techniques, namely, Power Spectral Density, and Short Time Fourier Transform. The signatures obtained were used to pinpoint the location of flaw formation. The results obtained in this study show that the effects of contamination in WAAM can be identified through the analysis of the acoustic spectrum of the process.