Low noise level is an essential feature when installing ventilation systems today. To achieve attenuation over a broad frequency range, the passive silencers traditionally used to attenuate ventilation noise can be combined with an active noise control (ANC) system. To insure reliable operation and desirable levels of attenuation when applying ANC to duct noise, it is highly important to be able to suppress the contamination of the microphone signals due to the turbulent pressure fluctuations, which arise as the microphones are exposed to the airflow in the duct. This paper is the first in a series of two regarding the problem of turbulence-induced noise originating from the airflow inside the ducts. Part I is concerned with theoretical and experimental investigations of the influence of the turbulence-induced noise on the adaptive algorithm in the ANC system. Part II is concerned with the design and the investigations of microphone installations for turbulence suppression and the results concerning the performance of an ANC system with the different microphone installations are presented. Some of the results were obtained at an acoustic laboratory according to an ISO-standard. The attenuation achieved with ANC was approximately 15-25 dB between 50-315 Hz, even for airflow speeds up to 20 m/s.
Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire.
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