Mid Sweden University

Indoor localization has been a popular research subject in recent years. Usually, object localization using sound involves devices on the objects, acquiring data from stationary sound sources, or by localizing the objects with external sensors when the object generates sounds. Indoor localization systems using microphones have traditionally also used systems with several microphones, setting the limitations on cost efficiency and required space for the systems. In this paper, the goal is to investigate whether it is possible for a stationary system to localize a silent object in a room, with only one microphone and ambient noise as information carrier. A subtraction method has been combined with a fingerprint technique, to define and distinguish the noise absorption characteristic of the silent object in the frequency domain for different object positions. The absorption characteristics of several positions of the object is taken as comparison references, serving as fingerprints of known positions for an object. With the experiment result, the tentative idea has been verified as feasible, and noise signal based lateral localization of silent objects can be achieved.

The sound source localization technology brings the possibility of mapping the sound source positions. In this paper, angle-of-arrival (AOA) has been chosen as the method for achieving sound source localization in an indoor enclosed environment. The dynamic environment and reverberations bring a challenge for AOA-based systems for such applications. By the acknowledgement of microphone directionality, the cardioid-directional microphone systems have been chosen for the localization performance comparison with omni-directional microphone systems, in order to investigate which microphone is superior in AOA indoor sound source localization. To reduce the hardware complexity, the number of microphones used during the experiment has been limited to 4. A localization improvement has been proposed with a weighting factor. The comparison has been done for both types of microphones with 3 different array manifolds under the same system setup. The comparison shows that the cardioid-directional microphone system has an overall higher accuracy. 

In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors.

In cardiac auscultation, the ability to clearly hear any existing murmur sounds in heart sounds is crucial for proper diagnosis. This work aims to improve heart sound by the use of a stethoscope array and beamforming technique. The stethoscope array comprises four piezo elements for measurement, placed on the edges of a 40mm by 40mm rectangle. The directionality of the piezo elements reduces the effect of ambient noise in the measurement. The signal amelioration is achieved by isolating the systole and diastole sounds, and independently applying the delay-and-sum beamforming. This thereby makes any existing murmur sounds in the systole and/or diastole more audible and clearer to aid diagnosis. Finally, the designed stethoscope array and signal processing shows a gain of up to 33% for measured healthy heart samples, and up to 63% increase in murmur sound gain for measured sample with medically confirmed murmur. 

The field of Metrology has seen great use of Machine Learning and Deep Learning models, improving existing Metrology and enabling measurements and estimations that were previously not possible. In the challenging task of gathering training data in various areas of Metrology, a question arises; is it necessary to gather a completely new dataset every time a quality upgrade is done to a measurement system, method or model, or can the formerly trained model be used for new data with higher Signal-to-Noise Ratio (SNR)? This paper investigates how trained neural networks react to new data coming into the testing, with a higher SNR than the training data. In the experiments, Convolutional Neural Networks (CNN), in 1D and 2D, are used on heart sound data, as a test case. The initial results show that the classification accuracy for the new data, with a higher SNR, coming into the 1D CNN is almost as high as if the network had been trained on the higher SNR data. For a 2D CNN working with spectrograms instead of time series data, the change in accuracy is not nearly as high, as the 2D CNN model seems more robust to noise differences.